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Methodology, Parameters, and Calculations

Parameter definitions, formulas, uncertainty ranges, and data sources.

Doi

Keywords

health economics methodology, clinical trial cost analysis, medical research ROI, cost-benefit analysis healthcare, sensitivity analysis, Monte Carlo simulation, DALY calculation, pragmatic clinical trials

Overview

This appendix documents all 119 parameters used in the analysis, organized by type:

External sources (peer-reviewed): 39
Calculated values: 50
Core definitions: 30

Calculated Values

Parameters derived from mathematical formulas and economic models.

Combination Therapy Space: 45.1B combinations

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Total combination therapy space (pairwise drug combinations × diseases). Standard in oncology, HIV, cardiology.

Inputs:

Pairwise Drug Combinations 🔢: 45.1M combinations
Trial-Relevant Diseases: 1.00k diseases (95% CI: 800 diseases - 1.20k diseases)

\[ \begin{gathered} Space_{combo} \\ = N_{combo} \times N_{diseases,trial} \\ = 45.1M \times 1{,}000 \\ = 45.1B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Pairwise Drug Combinations: 45.1M combinations

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Unique pairwise drug combinations from known safe compounds (n choose 2)

Inputs:

Safe Compounds Available for Testing: 9.50k compounds (95% CI: 7.00k compounds - 12.0k compounds)

Formula: SAFE_COMPOUNDS × (SAFE_COMPOUNDS - 1) ÷ 2

✓ High confidence

Sensitivity Analysis

Combination Therapy Exploration Time (Current): 13.7M years

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Years to test all pairwise drug combinations at current trial capacity. Combination therapy is standard in oncology, HIV, cardiology.

Inputs:

Combination Therapy Space 🔢: 45.1B combinations
Current Global Clinical Trials per Year 📊: 3.30k trials/year (95% CI: 2.64k trials/year - 3.96k trials/year)

\[ \begin{gathered} T_{explore,combo} \\ = \frac{Space_{combo}}{Trials_{ann,curr}} \\ = \frac{45.1B}{3{,}300} \\ = 13.7M \\[0.5em] \text{where } Space_{combo} \\ = N_{combo} \times N_{diseases,trial} \\ = 45.1M \times 1{,}000 \\ = 45.1B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Combination Therapy Exploration Time (Current)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Current Trials Per Year	-0.9931	Strong driver

Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.

Monte Carlo Distribution

Simulation Results Summary: Combination Therapy Exploration Time (Current)

Statistic	Value
Baseline (deterministic)	13.7M
Mean (expected value)	13.8M
Median (50th percentile)	13.8M
Standard Deviation	1.36M
90% Confidence Interval	[11.6M, 16.3M]

The histogram shows the distribution of Combination Therapy Exploration Time (Current) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Combination Therapy Exploration Time (Current)

This exceedance probability chart shows the likelihood that Combination Therapy Exploration Time (Current) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Known Safe Exploration Time (Current): 2.88k years

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Years to test all known safe drug-disease combinations at current global trial capacity

Inputs:

Possible Drug-Disease Combinations 🔢: 9.50M combinations
Current Global Clinical Trials per Year 📊: 3.30k trials/year (95% CI: 2.64k trials/year - 3.96k trials/year)

\[ \begin{gathered} T_{explore,safe} \\ = \frac{N_{combos}}{Trials_{ann,curr}} \\ = \frac{9.5M}{3{,}300} \\ = 2{,}880 \\[0.5em] \text{where } N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Known Safe Exploration Time (Current)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Current Trials Per Year	-0.9931	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Known Safe Exploration Time (Current)

Statistic	Value
Baseline (deterministic)	2.88k
Mean (expected value)	2.91k
Median (50th percentile)	2.90k
Standard Deviation	286
90% Confidence Interval	[2.45k, 3.43k]

The histogram shows the distribution of Known Safe Exploration Time (Current) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Known Safe Exploration Time (Current)

This exceedance probability chart shows the likelihood that Known Safe Exploration Time (Current) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Annual Decentralized Framework for Drug Assessment Operational Costs: $40M

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Total annual Decentralized Framework for Drug Assessment operational costs (sum of all components: $15M + $10M + $8M + $5M + $2M)

Inputs:

Decentralized Framework for Drug Assessment Maintenance Costs: $15M (95% CI: $10M - $22M)
Decentralized Framework for Drug Assessment Staff Costs: $10M (95% CI: $7M - $15M)
Decentralized Framework for Drug Assessment Infrastructure Costs: $8M (95% CI: $5M - $12M)
Decentralized Framework for Drug Assessment Regulatory Coordination Costs: $5M (95% CI: $3M - $8M)
Decentralized Framework for Drug Assessment Community Support Costs: $2M (95% CI: $1M - $3M)

\[ \begin{gathered} OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#opex-breakdown

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Total Annual Decentralized Framework for Drug Assessment Operational Costs

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA OPEX Platform Maintenance	0.3542	Moderate driver
dFDA OPEX Staff	0.2355	Weak driver
dFDA OPEX Infrastructure	0.2060	Weak driver
dFDA OPEX Regulatory	0.1469	Weak driver
dFDA OPEX Community	0.0576	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Annual Decentralized Framework for Drug Assessment Operational Costs

Statistic	Value
Baseline (deterministic)	$40M
Mean (expected value)	$39.9M
Median (50th percentile)	$39M
Standard Deviation	$8.21M
90% Confidence Interval	[$27.3M, $55.6M]

The histogram shows the distribution of Total Annual Decentralized Framework for Drug Assessment Operational Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Annual Decentralized Framework for Drug Assessment Operational Costs

This exceedance probability chart shows the likelihood that Total Annual Decentralized Framework for Drug Assessment Operational Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings: $58.6B

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Annual Decentralized Framework for Drug Assessment benefit from R&D savings (trial cost reduction, secondary component)

Inputs:

Annual Global Spending on Clinical Trials 📊: $60B (95% CI: $50B - $75B)
dFDA Trial Cost Reduction Percentage 🔢: 97.7%

\[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \\[0.5em] \text{where } Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#cost-reduction

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Clinical Trials Spending Annual	1.0205	Strong driver
dFDA Trial Cost Reduction %	0.0244	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings

Statistic	Value
Baseline (deterministic)	$58.6B
Mean (expected value)	$58.8B
Median (50th percentile)	$57.8B
Standard Deviation	$7.66B
90% Confidence Interval	[$49.2B, $73.1B]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total DALYs Lost from Disease Eradication Delay: 7.94B DALYs

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Total Disability-Adjusted Life Years lost from disease eradication delay (PRIMARY estimate)

Inputs:

Years of Life Lost from Disease Eradication Delay 🔢: 7.07B years
Years Lived with Disability During Disease Eradication Delay 🔢: 873M years

\[ \begin{gathered} DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.07B + 873M = 7.94B \\[0.5em] \text{where } YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

Methodology: ../appendix/regulatory-mortality-analysis#daly-calculation

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Total DALYs Lost from Disease Eradication Delay

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Efficacy Lag Elimination Yll	0.7043	Strong driver
dFDA Efficacy Lag Elimination Yld	0.3107	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Total DALYs Lost from Disease Eradication Delay

Statistic	Value
Baseline (deterministic)	7.94B
Mean (expected value)	8.05B
Median (50th percentile)	7.89B
Standard Deviation	2.31B
90% Confidence Interval	[4.43B, 12.1B]

The histogram shows the distribution of Total DALYs Lost from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total DALYs Lost from Disease Eradication Delay

This exceedance probability chart shows the likelihood that Total DALYs Lost from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Deaths from Disease Eradication Delay: 416M deaths

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Total eventually avoidable deaths from delaying disease eradication by 8.2 years (PRIMARY estimate, conservative). Excludes fundamentally unavoidable deaths (primarily accidents ~7.9%).

Inputs:

Regulatory Delay for Efficacy Testing Post-Safety Verification 📊: 8.2 years (SE: ±2 years)
Global Daily Deaths from Disease and Aging 📊: 150k deaths/day (SE: ±7.50k deaths/day)

\[ \begin{gathered} Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

Methodology: ../appendix/regulatory-mortality-analysis#disease-eradication-delay

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Total Deaths from Disease Eradication Delay

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Efficacy Lag Years	1.1404	Strong driver
Global Disease Deaths Daily	-0.1422	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Total Deaths from Disease Eradication Delay

Statistic	Value
Baseline (deterministic)	416M
Mean (expected value)	420M
Median (50th percentile)	414M
Standard Deviation	122M
90% Confidence Interval	[225M, 630M]

The histogram shows the distribution of Total Deaths from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Deaths from Disease Eradication Delay

This exceedance probability chart shows the likelihood that Total Deaths from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Economic Loss from Disease Eradication Delay: $1.19 quadrillion

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Total economic loss from delaying disease eradication by 8.2 years (PRIMARY estimate, 2024 USD). Values global DALYs at standardized US/International normative rate ($150k) rather than local ability-to-pay, representing the full human capital loss.

Inputs:

Total DALYs Lost from Disease Eradication Delay 🔢: 7.94B DALYs
Standard Economic Value per QALY 📊: $150K (SE: ±$30K)

\[ \begin{gathered} Value_{lag} \\ = DALYs_{lag} \times Value_{QALY} \\ = 7.94B \times \$150K \\ = \$1190T \\[0.5em] \text{where } DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.07B + 873M = 7.94B \\[0.5em] \text{where } YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

Methodology: ../appendix/regulatory-mortality-analysis#economic-valuation

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Total Economic Loss from Disease Eradication Delay

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Efficacy Lag Elimination DALYs	1.0671	Strong driver
Standard Economic QALY Value Usd	-0.0733	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Economic Loss from Disease Eradication Delay

Statistic	Value
Baseline (deterministic)	$1.19 quadrillion
Mean (expected value)	$1.27 quadrillion
Median (50th percentile)	$1.18 quadrillion
Standard Deviation	$581T
90% Confidence Interval	[$443T, $2.41 quadrillion]

The histogram shows the distribution of Total Economic Loss from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Economic Loss from Disease Eradication Delay

This exceedance probability chart shows the likelihood that Total Economic Loss from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Years Lived with Disability During Disease Eradication Delay: 873M years

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Years Lived with Disability during disease eradication delay (PRIMARY estimate)

Inputs:

Total Deaths from Disease Eradication Delay 🔢: 416M deaths
Pre-Death Suffering Period During Post-Safety Efficacy Delay 📊: 6 years (95% CI: 4 years - 9 years)
Disability Weight for Untreated Chronic Conditions 📊: 0.35 weight (SE: ±0.07 weight)

\[ \begin{gathered} YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

Methodology: ../appendix/regulatory-mortality-analysis#daly-calculation

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Years Lived with Disability During Disease Eradication Delay

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Regulatory Delay Suffering Period Years	2.0883	Strong driver
Chronic Disease Disability Weight	-0.9003	Strong driver
dFDA Efficacy Lag Elimination Deaths Averted	-0.2255	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Years Lived with Disability During Disease Eradication Delay

Statistic	Value
Baseline (deterministic)	873M
Mean (expected value)	1.02B
Median (50th percentile)	846M
Standard Deviation	716M
90% Confidence Interval	[217M, 2.43B]

The histogram shows the distribution of Years Lived with Disability During Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Years Lived with Disability During Disease Eradication Delay

This exceedance probability chart shows the likelihood that Years Lived with Disability During Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Years of Life Lost from Disease Eradication Delay: 7.07B years

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Years of Life Lost from disease eradication delay deaths (PRIMARY estimate)

Inputs:

Total Deaths from Disease Eradication Delay 🔢: 416M deaths
Global Life Expectancy (2024) 📊: 79 years (SE: ±2 years)
Mean Age of Preventable Death from Post-Safety Efficacy Delay 📊: 62 years (SE: ±3 years)

\[ \begin{gathered} YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

Methodology: ../appendix/regulatory-mortality-analysis#daly-calculation

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Years of Life Lost from Disease Eradication Delay

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Global Life Expectancy 2024	2.0066	Strong driver
Regulatory Delay Mean Age Of Death	-1.3852	Strong driver
dFDA Efficacy Lag Elimination Deaths Averted	0.3779	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Years of Life Lost from Disease Eradication Delay

Statistic	Value
Baseline (deterministic)	7.07B
Mean (expected value)	7.03B
Median (50th percentile)	7.05B
Standard Deviation	1.62B
90% Confidence Interval	[4.21B, 9.68B]

The histogram shows the distribution of Years of Life Lost from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Years of Life Lost from Disease Eradication Delay

This exceedance probability chart shows the likelihood that Years of Life Lost from Disease Eradication Delay will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA New Treatments Per Year: 185 diseases/year

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Diseases per year receiving their first effective treatment with dFDA. Scales proportionally with trial capacity multiplier.

Inputs:

Diseases Getting First Treatment Per Year 📊: 15 diseases/year (95% CI: 8 diseases/year - 30 diseases/year)
Trial Capacity Multiplier 🔢: 12.3:1

\[ \begin{gathered} Treatments_{dFDA,ann} \\ = Treatments_{new,ann} \times k_{capacity} \\ = 15 \times 12.3 \\ = 185 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for dFDA New Treatments Per Year

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Multiplier	0.9380	Strong driver
New Disease First Treatments Per Year	-0.0784	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: dFDA New Treatments Per Year

Statistic	Value
Baseline (deterministic)	185
Mean (expected value)	254
Median (50th percentile)	224
Standard Deviation	140
90% Confidence Interval	[107, 490]

The histogram shows the distribution of dFDA New Treatments Per Year across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA New Treatments Per Year

This exceedance probability chart shows the likelihood that dFDA New Treatments Per Year will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only): $58.6B

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Annual net savings from R&D cost reduction only (gross savings minus operational costs, excludes regulatory delay value)

Inputs:

Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings 🔢: $58.6B
Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M

\[ \begin{gathered} Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{dFDA} \\ = \$58.6B - \$40M \\ = \$58.6B \\[0.5em] \text{where } Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \\[0.5em] \text{where } Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#net-savings

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Benefit R&D Only Annual	1.0011	Strong driver
dFDA Annual OPEX	-0.0011	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only)

Statistic	Value
Baseline (deterministic)	$58.6B
Mean (expected value)	$58.8B
Median (50th percentile)	$57.8B
Standard Deviation	$7.66B
90% Confidence Interval	[$49.2B, $73B]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only)

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Total NPV Annual OPEX: $40M

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Total NPV annual opex (Decentralized Framework for Drug Assessment core + DIH initiatives)

Inputs:

Decentralized Framework for Drug Assessment Core framework Annual OPEX: $18.9M (95% CI: $11M - $26.5M)
DIH Broader Initiatives Annual OPEX: $21.1M (95% CI: $14M - $32M)

\[ \begin{gathered} OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#npv-costs

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Total NPV Annual OPEX

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
DIH NPV Annual OPEX Initiatives	0.5419	Strong driver
dFDA NPV Annual OPEX	0.4592	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Total NPV Annual OPEX

Statistic	Value
Baseline (deterministic)	$40M
Mean (expected value)	$39.9M
Median (50th percentile)	$39.1M
Standard Deviation	$8.04M
90% Confidence Interval	[$27.5M, $55.4M]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Total NPV Annual OPEX across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Total NPV Annual OPEX

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Total NPV Annual OPEX will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted): $389B

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NPV of Decentralized Framework for Drug Assessment R&D savings only with 5-year adoption ramp (10-year horizon, most conservative financial estimate)

Inputs:

Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only) 🔢: $58.6B
Standard Discount Rate for NPV Analysis: 3%

\[ \begin{gathered} NPV_{RD} \\ = \sum_{t=1}^{10} \frac{Savings_{RD,ann} \times \frac{\min(t,5)}{5}}{(1+r)^t} \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#npv-benefit

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Net Savings R&D Only Annual	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted)

Statistic	Value
Baseline (deterministic)	$389B
Mean (expected value)	$391B
Median (50th percentile)	$384B
Standard Deviation	$50.9B
90% Confidence Interval	[$327B, $485B]

The histogram shows the distribution of NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted)

This exceedance probability chart shows the likelihood that NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

NPV Net Benefit (R&D Only): $389B

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NPV net benefit using R&D savings only (benefits minus costs)

Inputs:

NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted) 🔢: $389B
Decentralized Framework for Drug Assessment Total NPV Cost 🔢: $611M

\[ \begin{gathered} NPV_{net,RD} \\ = NPV_{RD} - Cost_{dFDA,total} \\ = \$389B - \$611M \\ = \$389B \\[0.5em] \text{where } NPV_{RD} = \sum_{t=1}^{10} \frac{Savings_{RD,ann} \times \frac{\min(t,5)}{5}}{(1+r)^t} \\[0.5em] \text{where } Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{dFDA} \\ = \$58.6B - \$40M \\ = \$58.6B \\[0.5em] \text{where } Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \\[0.5em] \text{where } Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Cost_{dFDA,total} \\ = PV_{OPEX} + Cost_{upfront,total} \\ = \$342M + \$270M \\ = \$611M \\[0.5em] \text{where } PV_{OPEX} \\ = \frac{T_{horizon}}{OPEX_{total} \times r_{discount}} \\ = \frac{10}{\$40M \times 3\%} \\ = \$342M \\[0.5em] \text{where } OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \\[0.5em] \text{where } Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#npv-net-benefit

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for NPV Net Benefit (R&D Only)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA NPV Benefit R&D Only	1.0025	Strong driver
dFDA NPV Total Cost	-0.0025	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: NPV Net Benefit (R&D Only)

Statistic	Value
Baseline (deterministic)	$389B
Mean (expected value)	$390B
Median (50th percentile)	$383B
Standard Deviation	$50.7B
90% Confidence Interval	[$326B, $484B]

The histogram shows the distribution of NPV Net Benefit (R&D Only) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: NPV Net Benefit (R&D Only)

This exceedance probability chart shows the likelihood that NPV Net Benefit (R&D Only) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years: $342M

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Present value of annual opex over 10 years (NPV formula)

Inputs:

Decentralized Framework for Drug Assessment Total NPV Annual OPEX 🔢: $40M
Standard Discount Rate for NPV Analysis: 3%
Standard Time Horizon for NPV Analysis: 10 years

\[ PV_{OPEX} = OPEX_{ann} \times \frac{1 - (1+r)^{-T}}{r} \]

Methodology: ../appendix/dfda-impact-paper#npv-calculation

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA NPV Annual OPEX Total	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years

Statistic	Value
Baseline (deterministic)	$342M
Mean (expected value)	$340M
Median (50th percentile)	$333M
Standard Deviation	$68.6M
90% Confidence Interval	[$235M, $473M]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Total NPV Cost: $611M

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Total NPV cost (upfront + PV of annual opex)

Inputs:

Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years 🔢: $342M
Decentralized Framework for Drug Assessment Total NPV Upfront Costs 🔢: $270M

\[ \begin{gathered} Cost_{dFDA,total} \\ = PV_{OPEX} + Cost_{upfront,total} \\ = \$342M + \$270M \\ = \$611M \\[0.5em] \text{where } PV_{OPEX} \\ = \frac{T_{horizon}}{OPEX_{total} \times r_{discount}} \\ = \frac{10}{\$40M \times 3\%} \\ = \$342M \\[0.5em] \text{where } OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \\[0.5em] \text{where } Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#npv-total-cost

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Total NPV Cost

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA NPV Pv Annual OPEX	0.5417	Strong driver
dFDA NPV Upfront Cost Total	0.4585	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Total NPV Cost

Statistic	Value
Baseline (deterministic)	$611M
Mean (expected value)	$609M
Median (50th percentile)	$595M
Standard Deviation	$127M
90% Confidence Interval	[$415M, $853M]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Total NPV Cost across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Total NPV Cost

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Total NPV Cost will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Decentralized Framework for Drug Assessment Total NPV Upfront Costs: $270M

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Total NPV upfront costs (Decentralized Framework for Drug Assessment core + DIH initiatives)

Inputs:

Decentralized Framework for Drug Assessment Core framework Build Cost: $40M (95% CI: $25M - $65M)
DIH Broader Initiatives Upfront Cost: $230M (95% CI: $150M - $350M)

\[ \begin{gathered} Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#npv-costs

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Decentralized Framework for Drug Assessment Total NPV Upfront Costs

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
DIH NPV Upfront Cost Initiatives	0.8338	Strong driver
dFDA NPV Upfront Cost	0.1662	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Decentralized Framework for Drug Assessment Total NPV Upfront Costs

Statistic	Value
Baseline (deterministic)	$270M
Mean (expected value)	$269M
Median (50th percentile)	$262M
Standard Deviation	$58.1M
90% Confidence Interval	[$181M, $380M]

The histogram shows the distribution of Decentralized Framework for Drug Assessment Total NPV Upfront Costs across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Decentralized Framework for Drug Assessment Total NPV Upfront Costs

This exceedance probability chart shows the likelihood that Decentralized Framework for Drug Assessment Total NPV Upfront Costs will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Queue Clearance Time: 36 years

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Years to treat all currently untreatable diseases with dFDA implementation. Queue clearance time divided by trial capacity multiplier.

Inputs:

Status Quo Queue Clearance Time 🔢: 443 years
Trial Capacity Multiplier 🔢: 12.3:1

\[ \begin{gathered} T_{queue,dFDA} = \frac{T_{queue,SQ}}{k_{capacity}} = \frac{443}{12.3} = 36 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Queue Clearance Time

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Status Quo Queue Clearance Years	-1.3321	Strong driver
dFDA Trial Capacity Multiplier	0.4867	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: dFDA Queue Clearance Time

Statistic	Value
Baseline (deterministic)	36
Mean (expected value)	34.6
Median (50th percentile)	29.7
Standard Deviation	19.9
90% Confidence Interval	[11.6, 77.2]

The histogram shows the distribution of dFDA Queue Clearance Time across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Queue Clearance Time

This exceedance probability chart shows the likelihood that dFDA Queue Clearance Time will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Daily R&D Savings from Trial Cost Reduction: $161M

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Daily R&D savings from trial cost reduction (opportunity cost of delay)

Inputs:

Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings 🔢: $58.6B

\[ \begin{gathered} Savings_{RD,daily} \\ = Benefit_{RD,ann} \times 0.00274 \\ = \$58.6B \times 0.00274 \\ = \$161M \\[0.5em] \text{where } Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \\[0.5em] \text{where } Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#daily-opportunity-cost-of-inaction

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Daily R&D Savings from Trial Cost Reduction

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Benefit R&D Only Annual	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Daily R&D Savings from Trial Cost Reduction

Statistic	Value
Baseline (deterministic)	$161M
Mean (expected value)	$161M
Median (50th percentile)	$158M
Standard Deviation	$21M
90% Confidence Interval	[$135M, $200M]

The histogram shows the distribution of Daily R&D Savings from Trial Cost Reduction across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Daily R&D Savings from Trial Cost Reduction

This exceedance probability chart shows the likelihood that Daily R&D Savings from Trial Cost Reduction will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

ROI from Decentralized Framework for Drug Assessment R&D Savings Only: 637:1

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ROI from Decentralized Framework for Drug Assessment R&D savings only (10-year NPV, most conservative estimate)

Inputs:

NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted) 🔢: $389B
Decentralized Framework for Drug Assessment Total NPV Cost 🔢: $611M

\[ \begin{gathered} ROI_{RD} = \frac{NPV_{RD}}{Cost_{dFDA,total}} = \frac{\$389B}{\$611M} = 637 \\[0.5em] \text{where } NPV_{RD} = \sum_{t=1}^{10} \frac{Savings_{RD,ann} \times \frac{\min(t,5)}{5}}{(1+r)^t} \\[0.5em] \text{where } Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{dFDA} \\ = \$58.6B - \$40M \\ = \$58.6B \\[0.5em] \text{where } Benefit_{RD,ann} \\ = Spending_{trials} \times Reduce_{pct} \\ = \$60B \times 97.7\% \\ = \$58.6B \\[0.5em] \text{where } Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Cost_{dFDA,total} \\ = PV_{OPEX} + Cost_{upfront,total} \\ = \$342M + \$270M \\ = \$611M \\[0.5em] \text{where } PV_{OPEX} \\ = \frac{T_{horizon}}{OPEX_{total} \times r_{discount}} \\ = \frac{10}{\$40M \times 3\%} \\ = \$342M \\[0.5em] \text{where } OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \\[0.5em] \text{where } Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#roi-simple

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for ROI from Decentralized Framework for Drug Assessment R&D Savings Only

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA NPV Total Cost	-2.6305	Strong driver
dFDA NPV Benefit R&D Only	1.7615	Strong driver

Monte Carlo Distribution

Simulation Results Summary: ROI from Decentralized Framework for Drug Assessment R&D Savings Only

Statistic	Value
Baseline (deterministic)	637:1
Mean (expected value)	653:1
Median (50th percentile)	645:1
Standard Deviation	58.4:1
90% Confidence Interval	[569:1, 790:1]

The histogram shows the distribution of ROI from Decentralized Framework for Drug Assessment R&D Savings Only across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: ROI from Decentralized Framework for Drug Assessment R&D Savings Only

This exceedance probability chart shows the likelihood that ROI from Decentralized Framework for Drug Assessment R&D Savings Only will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Trial Capacity Multiplier: 12.3:1

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Trial capacity multiplier from DIH funding capacity vs. current global trial participation

Inputs:

Annual Global Clinical Trial Participants 📊: 1.90M patients/year (95% CI: 1.50M patients/year - 2.30M patients/year)
Patients Fundable Annually 🔢: 23.4M patients/year

\[ \begin{gathered} k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Trial Capacity Multiplier

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
DIH Patients Fundable Annually	1.0768	Strong driver
Current Trial Slots Available	0.0910	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Trial Capacity Multiplier

Statistic	Value
Baseline (deterministic)	12.3:1
Mean (expected value)	22.1:1
Median (50th percentile)	16:1
Standard Deviation	20.2:1
90% Confidence Interval	[4.19:1, 61.3:1]

The histogram shows the distribution of Trial Capacity Multiplier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Trial Capacity Multiplier

This exceedance probability chart shows the likelihood that Trial Capacity Multiplier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 565B DALYs

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Total DALYs averted from the combined dFDA timeline shift. Calculated as annual global DALY burden × eventually avoidable percentage × timeline shift years. Includes both fatal and non-fatal diseases (WHO GBD methodology).

Inputs:

Global Annual DALY Burden 📊: 2.88B DALYs/year (SE: ±150M DALYs/year)
Eventually Avoidable DALY Percentage: 92.6% (95% CI: 50% - 98%)
dFDA Average Total Timeline Shift 🔢: 212 years

\[ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Plus Efficacy Lag Years	0.9001	Strong driver
Eventually Avoidable DALY %	0.4864	Moderate driver
Global Annual DALY Burden	0.0433	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Statistic	Value
Baseline (deterministic)	565B
Mean (expected value)	610B
Median (50th percentile)	614B
Standard Deviation	148B
90% Confidence Interval	[361B, 877B]

The histogram shows the distribution of Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

This exceedance probability chart shows the likelihood that Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: $84.8 quadrillion

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Total economic value from the combined dFDA timeline shift. DALYs valued at standard economic rate.

Inputs:

Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput 🔢: 565B DALYs
Standard Economic Value per QALY 📊: $150K (SE: ±$30K)

\[ \begin{gathered} Value_{max} \\ = DALYs_{max} \times Value_{QALY} \\ = 565B \times \$150K \\ = \$84800T \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Plus Efficacy Lag DALYs	1.7790	Strong driver
Standard Economic QALY Value Usd	1.3377	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Statistic	Value
Baseline (deterministic)	$84.8 quadrillion
Mean (expected value)	$87.8 quadrillion
Median (50th percentile)	$92.8 quadrillion
Standard Deviation	$11.5 quadrillion
90% Confidence Interval	[$62.4 quadrillion, $97.3 quadrillion]

The histogram shows the distribution of Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

This exceedance probability chart shows the likelihood that Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 10.7B deaths

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Total eventually avoidable deaths from the combined dFDA timeline shift. Represents deaths prevented when cures arrive earlier due to both increased trial capacity and eliminated efficacy lag.

Inputs:

Global Daily Deaths from Disease and Aging 📊: 150k deaths/day (SE: ±7.50k deaths/day)
dFDA Average Total Timeline Shift 🔢: 212 years

\[ \begin{gathered} Lives_{max} \\ = Deaths_{disease,daily} \times T_{accel,max} \times 338 \\ = 150{,}000 \times 212 \times 338 \\ = 10.7B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Plus Efficacy Lag Years	1.0375	Strong driver
Global Disease Deaths Daily	0.0407	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Statistic	Value
Baseline (deterministic)	10.7B
Mean (expected value)	11.7B
Median (50th percentile)	11.7B
Standard Deviation	2.45B
90% Confidence Interval	[7.39B, 16.2B]

The histogram shows the distribution of Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

This exceedance probability chart shows the likelihood that Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput: 1931T hours

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Hours of suffering eliminated from the combined dFDA timeline shift. Calculated from YLD component of DALYs (39% of total DALYs × hours per year). One-time benefit, not annual recurring.

Inputs:

Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput 🔢: 565B DALYs
YLD Proportion of Total DALYs 📊: 0.39 proportion (SE: ±0.03 proportion)

\[ \begin{gathered} Hours_{suffer,max} \\ = DALYs_{max} \times Pct_{YLD} \times 8760 \\ = 565B \times 0.39 \times 8760 \\ = 1930T \\[0.5em] \text{where } DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \\[0.5em] \text{where } T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

Methodology: ../appendix/regulatory-mortality-analysis#daly-calculation

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Plus Efficacy Lag DALYs	1.3101	Strong driver
Global Yld Proportion Of DALYs	0.3975	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Statistic	Value
Baseline (deterministic)	1931T
Mean (expected value)	2049T
Median (50th percentile)	2107T
Standard Deviation	374T
90% Confidence Interval	[1362T, 2616T]

The histogram shows the distribution of Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

This exceedance probability chart shows the likelihood that Suffering Hours Eliminated from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Average Total Timeline Shift: 212 years

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Average years earlier patients receive treatments due to dFDA. Combines treatment timeline acceleration from increased trial capacity with efficacy lag elimination for treatments already discovered.

Inputs:

dFDA Treatment Timeline Acceleration 🔢: 204 years
Regulatory Delay for Efficacy Testing Post-Safety Verification 📊: 8.2 years (SE: ±2 years)

\[ \begin{gathered} T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \\[0.5em] \text{where } T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Average Total Timeline Shift

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Trial Capacity Treatment Acceleration Years	1.0325	Strong driver
Efficacy Lag Years	0.0327	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: dFDA Average Total Timeline Shift

Statistic	Value
Baseline (deterministic)	212
Mean (expected value)	233
Median (50th percentile)	231
Standard Deviation	60.3
90% Confidence Interval	[135, 355]

The histogram shows the distribution of dFDA Average Total Timeline Shift across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Average Total Timeline Shift

This exceedance probability chart shows the likelihood that dFDA Average Total Timeline Shift will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Treatment Timeline Acceleration: 204 years

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Years earlier the average first treatment arrives due to dFDA’s trial capacity increase. Calculated as the status quo timeline reduced by the inverse of the capacity multiplier. Uses only trial capacity multiplier (not combined with valley of death rescue) because additional candidates don’t directly speed queue processing.

Inputs:

Status Quo Average Years to First Treatment 🔢: 222 years
Trial Capacity Multiplier 🔢: 12.3:1

\[ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \\[0.5em] \text{where } T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \\[0.5em] \text{where } k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

? Low confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Treatment Timeline Acceleration

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Status Quo Avg Years To First Treatment	1.0665	Strong driver
dFDA Trial Capacity Multiplier	-0.0779	Minimal effect

Monte Carlo Distribution

Simulation Results Summary: dFDA Treatment Timeline Acceleration

Statistic	Value
Baseline (deterministic)	204
Mean (expected value)	225
Median (50th percentile)	223
Standard Deviation	62.3
90% Confidence Interval	[123, 350]

The histogram shows the distribution of dFDA Treatment Timeline Acceleration across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Treatment Timeline Acceleration

This exceedance probability chart shows the likelihood that dFDA Treatment Timeline Acceleration will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Trial Cost Reduction Factor: 44.1x

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Cost reduction factor projected for dFDA pragmatic trials ($41K traditional / $1,200 dFDA = 34x)

Inputs:

Phase 3 Cost per Patient 📊: $41K (95% CI: $20K - $120K)
dFDA Pragmatic Trial Cost per Patient 📊: $929 (95% CI: $97 - $3K)

\[ \begin{gathered} k_{reduce} \\ = \frac{Cost_{P3,pt}}{Cost_{pragmatic,pt}} \\ = \frac{\$41K}{\$929} \\ = 44.1 \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#cost-reduction

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Trial Cost Reduction Factor

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Pragmatic Trial Cost Per Patient	-8.8326	Strong driver
Traditional Phase3 Cost Per Patient	8.3341	Strong driver

Monte Carlo Distribution

Simulation Results Summary: dFDA Trial Cost Reduction Factor

Statistic	Value
Baseline (deterministic)	44.1x
Mean (expected value)	52.8x
Median (50th percentile)	48x
Standard Deviation	19.5x
90% Confidence Interval	[39.4x, 89.1x]

The histogram shows the distribution of dFDA Trial Cost Reduction Factor across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Trial Cost Reduction Factor

This exceedance probability chart shows the likelihood that dFDA Trial Cost Reduction Factor will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

dFDA Trial Cost Reduction Percentage: 97.7%

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Trial cost reduction percentage: (traditional - dFDA) / traditional = ($41K - $1.2K) / $41K = 97%

Inputs:

dFDA Pragmatic Trial Cost per Patient 📊: $929 (95% CI: $97 - $3K)
Phase 3 Cost per Patient 📊: $41K (95% CI: $20K - $120K)

\[ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} \]

Methodology: ../appendix/dfda-impact-paper#cost-reduction

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for dFDA Trial Cost Reduction Percentage

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Pragmatic Trial Cost Per Patient	-6.4207	Strong driver
Traditional Phase3 Cost Per Patient	5.6539	Strong driver

Monte Carlo Distribution

Simulation Results Summary: dFDA Trial Cost Reduction Percentage

Statistic	Value
Baseline (deterministic)	97.7%
Mean (expected value)	98%
Median (50th percentile)	97.9%
Standard Deviation	0.401%
90% Confidence Interval	[97.5%, 98.9%]

The histogram shows the distribution of dFDA Trial Cost Reduction Percentage across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: dFDA Trial Cost Reduction Percentage

This exceedance probability chart shows the likelihood that dFDA Trial Cost Reduction Percentage will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Patients Fundable Annually: 23.4M patients/year

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Number of patients fundable annually at dFDA pragmatic trial cost ($1,200/patient). Based on empirical pragmatic trial costs (RECOVERY to PCORnet range).

Inputs:

Annual Clinical Trial Patient Subsidies 🔢: $21.7B
dFDA Pragmatic Trial Cost per Patient 📊: $929 (95% CI: $97 - $3K)

\[ \begin{gathered} N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

Methodology: ../economics/1-pct-treaty-impact#funding-allocation

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Patients Fundable Annually

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
DIH Treasury Trial Subsidies Annual	2.3351	Strong driver
dFDA Pragmatic Trial Cost Per Patient	1.5755	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Patients Fundable Annually

Statistic	Value
Baseline (deterministic)	23.4M
Mean (expected value)	38.6M
Median (50th percentile)	30.2M
Standard Deviation	30.2M
90% Confidence Interval	[9.44M, 96.8M]

The histogram shows the distribution of Patients Fundable Annually across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Patients Fundable Annually

This exceedance probability chart shows the likelihood that Patients Fundable Annually will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Annual Clinical Trial Patient Subsidies: $21.7B

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Annual clinical trial patient subsidies (all medical research funds after Decentralized Framework for Drug Assessment operations)

Inputs:

Total Annual Decentralized Framework for Drug Assessment Operational Costs 🔢: $40M
Annual Funding for Pragmatic Clinical Trials: $21.8B

\[ \begin{gathered} Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]

Methodology: ../economics/1-pct-treaty-impact#funding-allocation

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Annual Clinical Trial Patient Subsidies

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Annual OPEX	-1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Annual Clinical Trial Patient Subsidies

Statistic	Value
Baseline (deterministic)	$21.7B
Mean (expected value)	$21.7B
Median (50th percentile)	$21.7B
Standard Deviation	$8.21M
90% Confidence Interval	[$21.7B, $21.7B]

The histogram shows the distribution of Annual Clinical Trial Patient Subsidies across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Annual Clinical Trial Patient Subsidies

This exceedance probability chart shows the likelihood that Annual Clinical Trial Patient Subsidies will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Diseases Without Effective Treatment: 6.65k diseases

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Number of diseases without effective treatment. 95% of 7,000 rare diseases lack FDA-approved treatment (per Orphanet 2024). This is the ‘queue’ of diseases waiting for cures.

Inputs:

Total Number of Rare Diseases Globally 📊: 7.00k diseases (95% CI: 6.00k diseases - 10.0k diseases)

\[ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} \]

Methodology: Orphanet Journal of Rare Diseases (2024) (2024) - Rare Disease Treatment Gap

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Diseases Without Effective Treatment

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Rare Diseases Count Global	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Diseases Without Effective Treatment

Statistic	Value
Baseline (deterministic)	6.65k
Mean (expected value)	6.73k
Median (50th percentile)	6.64k
Standard Deviation	835
90% Confidence Interval	[5.70k, 8.24k]

The histogram shows the distribution of Diseases Without Effective Treatment across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Diseases Without Effective Treatment

This exceedance probability chart shows the likelihood that Diseases Without Effective Treatment will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Possible Drug-Disease Combinations: 9.50M combinations

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Total possible drug-disease combinations using existing safe compounds

Inputs:

Safe Compounds Available for Testing: 9.50k compounds (95% CI: 7.00k compounds - 12.0k compounds)
Trial-Relevant Diseases: 1.00k diseases (95% CI: 800 diseases - 1.20k diseases)

\[ \begin{gathered} N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} \]

Methodology: ../problem/untapped-therapeutic-frontier

✓ High confidence

Sensitivity Analysis

Therapeutic Frontier Exploration Ratio: 0.342%

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Fraction of possible drug-disease space actually tested (<1%)

Inputs:

Tested Drug-Disease Relationships: 32.5k relationships (95% CI: 15.0k relationships - 50.0k relationships)
Possible Drug-Disease Combinations 🔢: 9.50M combinations

\[ \begin{gathered} Ratio_{explore} = \frac{N_{tested}}{N_{combos}} = \frac{32{,}500}{9.5M} = 0.342\% \\[0.5em] \text{where } N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} \]

Methodology: ../problem/untapped-therapeutic-frontier

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Therapeutic Frontier Exploration Ratio

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Tested Relationships Estimate	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Therapeutic Frontier Exploration Ratio

Statistic	Value
Baseline (deterministic)	0.342%
Mean (expected value)	0.339%
Median (50th percentile)	0.329%
Standard Deviation	0.0868%
90% Confidence Interval	[0.21%, 0.514%]

The histogram shows the distribution of Therapeutic Frontier Exploration Ratio across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Therapeutic Frontier Exploration Ratio

This exceedance probability chart shows the likelihood that Therapeutic Frontier Exploration Ratio will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Pragmatic Trial Cost per QALY (RECOVERY): $4.00

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Cost per QALY for pragmatic platform trials, calculated from RECOVERY trial data. Uses global impact methodology: trial cost divided by total QALYs from downstream adoption. This measures research efficiency (discovery value), not clinical intervention ICER.

Inputs:

RECOVERY Trial Total Cost 📊: $20M (95% CI: $15M - $25M)
RECOVERY Trial Total QALYs Generated 🔢: 5.00M QALYs

\[ \begin{gathered} Cost_{pragmatic,QALY} = \frac{Cost_{RECOVERY}}{QALY_{RECOVERY}} = \frac{\$20M}{5M} = \$4 \\[0.5em] \text{where } QALY_{RECOVERY} \\ = Lives_{RECOVERY} \times QALY_{COVID} \\ = 1M \times 5 \\ = 5M \end{gathered} \]

Methodology: Manhattan Institute - RECOVERY trial 82× cost reduction

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Pragmatic Trial Cost per QALY (RECOVERY)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Recovery Trial Total Cost	-1.4871	Strong driver
Recovery Trial Total QALYs Generated	0.5682	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Pragmatic Trial Cost per QALY (RECOVERY)

Statistic	Value
Baseline (deterministic)	$4.00
Mean (expected value)	$5.10
Median (50th percentile)	$4.55
Standard Deviation	$2.59
90% Confidence Interval	[$1.71, $10]

The histogram shows the distribution of Pragmatic Trial Cost per QALY (RECOVERY) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Pragmatic Trial Cost per QALY (RECOVERY)

This exceedance probability chart shows the likelihood that Pragmatic Trial Cost per QALY (RECOVERY) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Pragmatic Trial Efficiency Multiplier vs NIH: 12.5k:1

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How many times more cost-effective pragmatic trials are vs standard NIH research. Calculated using global impact methodology (NIH cost per QALY / pragmatic cost per QALY). Shows orders-of-magnitude efficiency gap between discovery-focused pragmatic trials and standard research.

Inputs:

NIH Standard Research Cost per QALY 📊: $50K (95% CI: $20K - $100K)
Pragmatic Trial Cost per QALY (RECOVERY) 🔢: $4.00

\[ \begin{gathered} k_{pragmatic:NIH} \\ = \frac{Cost_{NIH,QALY}}{Cost_{pragmatic,QALY}} \\ = \frac{\$50K}{\$4} \\ = 12{,}500 \\[0.5em] \text{where } Cost_{pragmatic,QALY} = \frac{Cost_{RECOVERY}}{QALY_{RECOVERY}} = \frac{\$20M}{5M} = \$4 \\[0.5em] \text{where } QALY_{RECOVERY} \\ = Lives_{RECOVERY} \times QALY_{COVID} \\ = 1M \times 5 \\ = 5M \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Pragmatic Trial Efficiency Multiplier vs NIH

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
NIH Standard Research Cost Per QALY	1.5607	Strong driver
Pragmatic Trial Cost Per QALY	0.6777	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Pragmatic Trial Efficiency Multiplier vs NIH

Statistic	Value
Baseline (deterministic)	12.5k:1
Mean (expected value)	15.8k:1
Median (50th percentile)	10.1k:1
Standard Deviation	16.2k:1
90% Confidence Interval	[2.26k:1, 51.5k:1]

The histogram shows the distribution of Pragmatic Trial Efficiency Multiplier vs NIH across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Pragmatic Trial Efficiency Multiplier vs NIH

This exceedance probability chart shows the likelihood that Pragmatic Trial Efficiency Multiplier vs NIH will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

RECOVERY Trial Total QALYs Generated: 5.00M QALYs

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Total QALYs generated by RECOVERY trial’s discoveries (lives saved × QALYs per life). Uses global impact methodology: counts all downstream health gains from the discovery.

Inputs:

RECOVERY Trial Global Lives Saved 📊: 1.00M lives (95% CI: 500k lives - 2.00M lives)
QALYs per COVID Death Averted: 5 QALYs/death (95% CI: 3 QALYs/death - 10 QALYs/death)

\[ \begin{gathered} QALY_{RECOVERY} \\ = Lives_{RECOVERY} \times QALY_{COVID} \\ = 1M \times 5 \\ = 5M \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for RECOVERY Trial Total QALYs Generated

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
QALYs Per Covid Death Averted	2.2404	Strong driver
Recovery Trial Global Lives Saved	-1.2571	Strong driver

Monte Carlo Distribution

Simulation Results Summary: RECOVERY Trial Total QALYs Generated

Statistic	Value
Baseline (deterministic)	5.00M
Mean (expected value)	5.57M
Median (50th percentile)	4.36M
Standard Deviation	4.03M
90% Confidence Interval	[1.51M, 14.3M]

The histogram shows the distribution of RECOVERY Trial Total QALYs Generated across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: RECOVERY Trial Total QALYs Generated

This exceedance probability chart shows the likelihood that RECOVERY Trial Total QALYs Generated will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Status Quo Average Years to First Treatment: 222 years

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Average years until first treatment discovered for a typical disease under current system. The average disease is in the middle of the queue, so it waits half the total queue clearance time (~443/2 = ~222 years).

Inputs:

Status Quo Queue Clearance Time 🔢: 443 years

\[ \begin{gathered} T_{first,SQ} = T_{queue,SQ} \times 0.5 = 443 \times 0.5 = 222 \\[0.5em] \text{where } T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \end{gathered} \]

Methodology: Composite estimate based on Orphanet - Average Time to Cure Under Current System

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Status Quo Average Years to First Treatment

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Status Quo Queue Clearance Years	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Status Quo Average Years to First Treatment

Statistic	Value
Baseline (deterministic)	222
Mean (expected value)	242
Median (50th percentile)	237
Standard Deviation	53.2
90% Confidence Interval	[162, 356]

The histogram shows the distribution of Status Quo Average Years to First Treatment across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Status Quo Average Years to First Treatment

This exceedance probability chart shows the likelihood that Status Quo Average Years to First Treatment will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Status Quo Queue Clearance Time: 443 years

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Years to clear entire queue of diseases without treatment. At current rate of ~15 diseases/year getting first treatments, the queue of ~6,650 would take ~443 years to completely clear.

Inputs:

Diseases Without Effective Treatment 🔢: 6.65k diseases
Diseases Getting First Treatment Per Year 📊: 15 diseases/year (95% CI: 8 diseases/year - 30 diseases/year)

\[ \begin{gathered} T_{queue,SQ} = \frac{N_{untreated}}{Treatments_{new,ann}} = \frac{6{,}650}{15} = 443 \\[0.5em] \text{where } N_{untreated} = N_{rare} \times 0.95 = 7{,}000 \times 0.95 = 6{,}650 \end{gathered} \]

Methodology: Composite estimate based on Orphanet - Average Time to Cure Under Current System

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Status Quo Queue Clearance Time

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Diseases Without Effective Treatment	-0.7011	Strong driver
New Disease First Treatments Per Year	-0.2360	Weak driver

Monte Carlo Distribution

Simulation Results Summary: Status Quo Queue Clearance Time

Statistic	Value
Baseline (deterministic)	443
Mean (expected value)	485
Median (50th percentile)	475
Standard Deviation	106
90% Confidence Interval	[324, 712]

The histogram shows the distribution of Status Quo Queue Clearance Time across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Status Quo Queue Clearance Time

This exceedance probability chart shows the likelihood that Status Quo Queue Clearance Time will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide DALYs Per Event: 41.8k DALYs

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Total DALYs per US-scale thalidomide event (YLL + YLD)

Inputs:

Thalidomide YLD Per Event 🔢: 13.0k years
Thalidomide YLL Per Event 🔢: 28.8k years

\[ \begin{gathered} DALY_{thal} \\ = YLD_{thal} + YLL_{thal} \\ = 13{,}000 + 28{,}800 \\ = 41{,}800 \\[0.5em] \text{where } YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \\[0.5em] \text{where } N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \\[0.5em] \text{where } YLL_{thal} = Deaths_{thal} \times 80 = 360 \times 80 = 28{,}800 \\[0.5em] \text{where } Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide DALYs Per Event

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide Yll Per Event	0.6300	Strong driver
Thalidomide Yld Per Event	0.3701	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide DALYs Per Event

Statistic	Value
Baseline (deterministic)	41.8k
Mean (expected value)	42.5k
Median (50th percentile)	40.8k
Standard Deviation	12.2k
90% Confidence Interval	[24.8k, 67.1k]

The histogram shows the distribution of Thalidomide DALYs Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide DALYs Per Event

This exceedance probability chart shows the likelihood that Thalidomide DALYs Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide Deaths Per Event: 360 deaths

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Deaths per US-scale thalidomide event

Inputs:

Thalidomide Mortality Rate 📊: 40% (95% CI: 35% - 45%)
Thalidomide US Cases Prevented 🔢: 900 cases

\[ \begin{gathered} Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide Deaths Per Event

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide US Cases Prevented	1.5027	Strong driver
Thalidomide Mortality Rate	-0.5048	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide Deaths Per Event

Statistic	Value
Baseline (deterministic)	360
Mean (expected value)	364
Median (50th percentile)	353
Standard Deviation	95.8
90% Confidence Interval	[223, 556]

The histogram shows the distribution of Thalidomide Deaths Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide Deaths Per Event

This exceedance probability chart shows the likelihood that Thalidomide Deaths Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide Survivors Per Event: 540 cases

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Survivors per US-scale thalidomide event

Inputs:

Thalidomide Mortality Rate 📊: 40% (95% CI: 35% - 45%)
Thalidomide US Cases Prevented 🔢: 900 cases

\[ \begin{gathered} N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide Survivors Per Event

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide Mortality Rate	0.5607	Strong driver
Thalidomide US Cases Prevented	0.4398	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide Survivors Per Event

Statistic	Value
Baseline (deterministic)	540
Mean (expected value)	537
Median (50th percentile)	531
Standard Deviation	86.3
90% Confidence Interval	[399, 698]

The histogram shows the distribution of Thalidomide Survivors Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide Survivors Per Event

This exceedance probability chart shows the likelihood that Thalidomide Survivors Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide US Cases Prevented: 900 cases

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Estimated US thalidomide cases prevented by FDA rejection

Inputs:

Thalidomide Cases Worldwide 📊: 15.0k cases (95% CI: 10.0k cases - 20.0k cases)
US Population Share 1960 📊: 6% (95% CI: 5.5% - 6.5%)

\[ \begin{gathered} N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide US Cases Prevented

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide Cases Worldwide	1.3746	Strong driver
Thalidomide US Population Share 1960	-0.3756	Moderate driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide US Cases Prevented

Statistic	Value
Baseline (deterministic)	900
Mean (expected value)	901
Median (50th percentile)	884
Standard Deviation	182
90% Confidence Interval	[622, 1.25k]

The histogram shows the distribution of Thalidomide US Cases Prevented across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide US Cases Prevented

This exceedance probability chart shows the likelihood that Thalidomide US Cases Prevented will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide YLD Per Event: 13.0k years

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Years Lived with Disability per thalidomide event

Inputs:

Thalidomide Disability Weight 📊: 0.4:1 (95% CI: 0.32:1 - 0.48:1)
Thalidomide Survivors Per Event 🔢: 540 cases
Thalidomide Survivor Lifespan 📊: 60 years (95% CI: 50 years - 70 years)

\[ \begin{gathered} YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \\[0.5em] \text{where } N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide YLD Per Event

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide Disability Weight	28.4785	Strong driver
Thalidomide Survivor Lifespan	-23.4440	Strong driver
Thalidomide Survivors Per Event	-4.0444	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide YLD Per Event

Statistic	Value
Baseline (deterministic)	13.0k
Mean (expected value)	13.3k
Median (50th percentile)	12.6k
Standard Deviation	4.50k
90% Confidence Interval	[6.94k, 22.6k]

The histogram shows the distribution of Thalidomide YLD Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide YLD Per Event

This exceedance probability chart shows the likelihood that Thalidomide YLD Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Thalidomide YLL Per Event: 28.8k years

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Years of Life Lost per thalidomide event (infant deaths)

Inputs:

Thalidomide Deaths Per Event 🔢: 360 deaths

\[ \begin{gathered} YLL_{thal} = Deaths_{thal} \times 80 = 360 \times 80 = 28{,}800 \\[0.5em] \text{where } Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Thalidomide YLL Per Event

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide Deaths Per Event	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Thalidomide YLL Per Event

Statistic	Value
Baseline (deterministic)	28.8k
Mean (expected value)	29.2k
Median (50th percentile)	28.2k
Standard Deviation	7.67k
90% Confidence Interval	[17.9k, 44.5k]

The histogram shows the distribution of Thalidomide YLL Per Event across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Thalidomide YLL Per Event

This exceedance probability chart shows the likelihood that Thalidomide YLL Per Event will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Ratio of Type Ii Error Cost to Type I Error Benefit: 3.07k:1

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Ratio of Type II error cost to Type I error benefit (harm from delay vs. harm prevented)

Inputs:

Total DALYs Lost from Disease Eradication Delay 🔢: 7.94B DALYs
Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) 🔢: 2.59M DALYs

\[ \begin{gathered} Ratio_{TypeII} = \frac{DALYs_{lag}}{DALY_{TypeI}} = \frac{7.94B}{2.59M} = 3{,}070 \\[0.5em] \text{where } DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.07B + 873M = 7.94B \\[0.5em] \text{where } YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } DALY_{TypeI} = DALY_{thal} \times 62 = 41{,}800 \times 62 = 2.59M \\[0.5em] \text{where } DALY_{thal} \\ = YLD_{thal} + YLL_{thal} \\ = 13{,}000 + 28{,}800 \\ = 41{,}800 \\[0.5em] \text{where } YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \\[0.5em] \text{where } N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \\[0.5em] \text{where } YLL_{thal} = Deaths_{thal} \times 80 = 360 \times 80 = 28{,}800 \\[0.5em] \text{where } Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

Methodology: ../appendix/regulatory-mortality-analysis#risk-analysis

~ Medium confidence

Sensitivity Analysis

Sensitivity Indices for Ratio of Type Ii Error Cost to Type I Error Benefit

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
dFDA Efficacy Lag Elimination DALYs	7.2872	Strong driver
Type I Error Benefit DALYs	-7.1207	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Ratio of Type Ii Error Cost to Type I Error Benefit

Statistic	Value
Baseline (deterministic)	3.07k:1
Mean (expected value)	3.05k:1
Median (50th percentile)	3.09k:1
Standard Deviation	101:1
90% Confidence Interval	[2.88k:1, 3.12k:1]

The histogram shows the distribution of Ratio of Type Ii Error Cost to Type I Error Benefit across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Ratio of Type Ii Error Cost to Type I Error Benefit

This exceedance probability chart shows the likelihood that Ratio of Type Ii Error Cost to Type I Error Benefit will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024): 2.59M DALYs

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Maximum DALYs saved by FDA preventing unsafe drugs over 62-year period 1962-2024 (extreme overestimate: one Thalidomide-scale event per year)

Inputs:

Thalidomide DALYs Per Event 🔢: 41.8k DALYs

\[ \begin{gathered} DALY_{TypeI} = DALY_{thal} \times 62 = 41{,}800 \times 62 = 2.59M \\[0.5em] \text{where } DALY_{thal} \\ = YLD_{thal} + YLL_{thal} \\ = 13{,}000 + 28{,}800 \\ = 41{,}800 \\[0.5em] \text{where } YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \\[0.5em] \text{where } N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \\[0.5em] \text{where } YLL_{thal} = Deaths_{thal} \times 80 = 360 \times 80 = 28{,}800 \\[0.5em] \text{where } Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

Methodology: ../appendix/regulatory-mortality-analysis#risk-analysis

? Low confidence

Sensitivity Analysis

Sensitivity Indices for Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Thalidomide DALYs Per Event	1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)

Statistic	Value
Baseline (deterministic)	2.59M
Mean (expected value)	2.63M
Median (50th percentile)	2.53M
Standard Deviation	754k
90% Confidence Interval	[1.54M, 4.16M]

The histogram shows the distribution of Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024)

This exceedance probability chart shows the likelihood that Maximum DALYs Saved by FDA Preventing Unsafe Drugs (1962-2024) will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

Unexplored Therapeutic Frontier: 99.7%

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Fraction of possible drug-disease space that remains unexplored (>99%)

Inputs:

Tested Drug-Disease Relationships: 32.5k relationships (95% CI: 15.0k relationships - 50.0k relationships)
Possible Drug-Disease Combinations 🔢: 9.50M combinations

\[ \begin{gathered} Ratio_{unexplored} \\ = 1 - \frac{N_{tested}}{N_{combos}} \\ = 1 - \frac{32{,}500}{9.5M} \\ = 99.7\% \\[0.5em] \text{where } N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} \]

Methodology: ../problem/untapped-therapeutic-frontier

✓ High confidence

Sensitivity Analysis

Sensitivity Indices for Unexplored Therapeutic Frontier

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Tested Relationships Estimate	-1.0000	Strong driver

Monte Carlo Distribution

Simulation Results Summary: Unexplored Therapeutic Frontier

Statistic	Value
Baseline (deterministic)	99.7%
Mean (expected value)	99.7%
Median (50th percentile)	99.7%
Standard Deviation	0.0868%
90% Confidence Interval	[99.5%, 99.8%]

The histogram shows the distribution of Unexplored Therapeutic Frontier across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Exceedance Probability

Probability of Exceeding Threshold: Unexplored Therapeutic Frontier

This exceedance probability chart shows the likelihood that Unexplored Therapeutic Frontier will exceed any given threshold. Higher curves indicate more favorable outcomes with greater certainty.

External Data Sources

Parameters sourced from peer-reviewed publications, institutional databases, and authoritative reports.

ADAPTABLE Trial Cost per Patient: $929

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Cost per patient in ADAPTABLE trial ($14M PCORI grant / 15,076 patients). Note: This is the direct grant cost; true cost including in-kind may be 10-40% higher.

Source: NIH Common Fund (2025) - NIH Pragmatic Trials: Minimal Funding Despite 30x Cost Advantage

Uncertainty Range

Technical: 95% CI: [$929, $1.40K] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between $929 and $1.40K (±25%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: ADAPTABLE Trial Cost per Patient

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence

ADAPTABLE Trial Total Cost: $14M

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PCORI grant for ADAPTABLE trial (2016-2019). Note: Direct funding only; total costs including site overhead and in-kind contributions from health systems may be higher.

Source: NIH Common Fund (2025) - NIH Pragmatic Trials: Minimal Funding Despite 30x Cost Advantage

Uncertainty Range

Technical: 95% CI: [$14M, $20M] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between $14M and $20M (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: ADAPTABLE Trial Total Cost

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence

Antidepressant Trial Exclusion Rate: 86.1%

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Mean exclusion rate in antidepressant trials (86.1% of real-world patients excluded)

Source: NIH (2015) - Antidepressant clinical trial exclusion rates

✓ High confidence

Bed Nets Cost per DALY: $89

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GiveWell cost per DALY for insecticide-treated bed nets (midpoint estimate, range $78-100). DALYs (Disability-Adjusted Life Years) measure disease burden by combining years of life lost and years lived with disability. Bed nets prevent malaria deaths and are considered a gold standard benchmark for cost-effective global health interventions - if an intervention costs less per DALY than bed nets, it’s exceptionally cost-effective. GiveWell synthesizes peer-reviewed academic research with transparent, rigorous methodology and extensive external expert review.

Source: GiveWell - GiveWell Cost per Life Saved for Top Charities (2024)

Uncertainty Range

Technical: 95% CI: [$78, $100] • Distribution: Normal

What this means: This estimate has moderate uncertainty. The true value likely falls between $78 and $100 (±12%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The normal distribution means values cluster around the center with equal chances of being higher or lower.

Input Distribution

Probability Distribution: Bed Nets Cost per DALY

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence • 📊 Peer-reviewed

Disability Weight for Untreated Chronic Conditions: 0.35 weight

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Disability weight for untreated chronic conditions (WHO Global Burden of Disease)

Source: World Health Organization (2024) - WHO Global Health Estimates 2024

Uncertainty Range

Technical: Distribution: Normal (SE: 0.07 weight)

Input Distribution

Probability Distribution: Disability Weight for Untreated Chronic Conditions

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence • 📊 Peer-reviewed

Current Clinical Trial Participation Rate: 0.06%

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Current clinical trial participation rate (0.06% of population)

Source: ACS CAN - Clinical trial patient participation rate

✓ High confidence

Global Population with Chronic Diseases: 2.40B people

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Global population with chronic diseases

Source: ScienceDaily (2015) - Global prevalence of chronic disease

Uncertainty Range

Technical: 95% CI: [2.00B people, 2.80B people] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between 2.00B people and 2.80B people (±17%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Global Population with Chronic Diseases

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Current Global Clinical Trials per Year: 3.30k trials/year

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Current global clinical trials per year

Source: Research and Markets (2024) - Global clinical trials market 2024

Uncertainty Range

Technical: 95% CI: [2.64k trials/year, 3.96k trials/year] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between 2.64k trials/year and 3.96k trials/year (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Current Global Clinical Trials per Year

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Annual Global Clinical Trial Participants: 1.90M patients/year

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Annual global clinical trial participants (IQVIA 2022: 1.9M post-COVID normalization)

Source: IQVIA Report - Global trial capacity

Uncertainty Range

Technical: 95% CI: [1.50M patients/year, 2.30M patients/year] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between 1.50M patients/year and 2.30M patients/year (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Annual Global Clinical Trial Participants

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

dFDA Pragmatic Trial Cost per Patient: $929

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dFDA pragmatic trial cost per patient. Uses ADAPTABLE trial ($929) as DELIBERATELY CONSERVATIVE central estimate. Harvard meta-analysis of 108 trials found median of only $97/patient - our estimate may overstate costs by 10x. Confidence interval spans meta-analysis median to complex chronic disease trials.

Source: NIH Common Fund (2025) - NIH Pragmatic Trials: Minimal Funding Despite 30x Cost Advantage

Uncertainty Range

Technical: 95% CI: [$97, $3K] • Distribution: Lognormal

What this means: This estimate is highly uncertain. The true value likely falls between $97 and $3K (±156%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: dFDA Pragmatic Trial Cost per Patient

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence

Drug Repurposing Success Rate: 30%

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Percentage of drugs that gain at least one new indication after initial approval

Source: Nature Medicine (2024) - Drug Repurposing Rate (~30%)

✓ High confidence

Regulatory Delay for Efficacy Testing Post-Safety Verification: 8.2 years

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Regulatory delay for efficacy testing (Phase II/III) post-safety verification. Based on BIO 2021 industry survey. Note: This is for drugs that COMPLETE the pipeline - survivor bias means actual delay for any given disease may be longer if candidates fail and must restart.

Source: Biotechnology Innovation Organization (BIO) (2021) - BIO Clinical Development Success Rates 2011-2020

Uncertainty Range

Technical: Distribution: Normal (SE: 2 years)

Input Distribution

Probability Distribution: Regulatory Delay for Efficacy Testing Post-Safety Verification

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence • 📊 Peer-reviewed • Updated 2021

Global Annual DALY Burden: 2.88B DALYs/year

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Global annual DALY burden from all diseases and injuries (WHO/IHME Global Burden of Disease 2021). Includes both YLL (years of life lost) and YLD (years lived with disability) from all causes.

Source: Institute for Health Metrics and Evaluation (IHME) (2024) - IHME Global Burden of Disease 2021 (2.88B DALYs, 1.13B YLD)

Uncertainty Range

Technical: Distribution: Normal (SE: 150M DALYs/year)

Input Distribution

Probability Distribution: Global Annual DALY Burden

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence • 📊 Peer-reviewed

Annual Global Spending on Clinical Trials: $60B

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Annual global spending on clinical trials (Industry: $45-60B + Government: $3-6B + Nonprofits: $2-5B). Conservative estimate using 15-20% of $300B total pharma R&D, not inflated market size projections.

Source: Derived from global market size and public/private funding ratios - Private industry clinical trial spending

Uncertainty Range

Technical: 95% CI: [$50B, $75B] • Distribution: Lognormal (SE: $10B)

What this means: This estimate has moderate uncertainty. The true value likely falls between $50B and $75B (±21%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Annual Global Spending on Clinical Trials

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Global Daily Deaths from Disease and Aging: 150k deaths/day

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Total global deaths per day from all disease and aging (WHO Global Burden of Disease 2024)

Source: World Health Organization (2024) - WHO Global Health Estimates 2024

Uncertainty Range

Technical: Distribution: Normal (SE: 7.50k deaths/day)

Input Distribution

Probability Distribution: Global Daily Deaths from Disease and Aging

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence • 📊 Peer-reviewed

Global Life Expectancy (2024): 79 years

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Global life expectancy (2024)

Source: World Health Organization (2024) - WHO Global Health Estimates 2024

Uncertainty Range

Technical: Distribution: Normal (SE: 2 years)

Input Distribution

Probability Distribution: Global Life Expectancy (2024)

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence • 📊 Peer-reviewed • Updated 2024

Global Military Spending in 2024: $2.72T

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Global military spending in 2024

Source: SIPRI (2025) - Global military spending ($2.72T, 2024)

Uncertainty Range

Technical: Distribution: Fixed

✓ High confidence

YLD Proportion of Total DALYs: 0.39 proportion

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Proportion of global DALYs that are YLD (years lived with disability) vs YLL (years of life lost). From GBD 2021: 1.13B YLD out of 2.88B total DALYs = 39%.

Source: Institute for Health Metrics and Evaluation (IHME) (2024) - IHME Global Burden of Disease 2021 (2.88B DALYs, 1.13B YLD)

Uncertainty Range

Technical: Distribution: Normal (SE: 0.03 proportion)

Input Distribution

Probability Distribution: YLD Proportion of Total DALYs

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence • 📊 Peer-reviewed

Human Interactome Targeted by Drugs: 12%

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Percentage of human interactome (protein-protein interactions) targeted by drugs

Source: PMC (2023) - Only ~12% of human interactome targeted

✓ High confidence

Diseases Getting First Treatment Per Year: 15 diseases/year

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Number of diseases that receive their FIRST effective treatment each year under current system. ~9 rare diseases/year (based on 40 years of ODA: 350 with treatment ÷ 40 years), plus ~5-10 common diseases. Note: FDA approves ~50 drugs/year, but most are for diseases that already have treatments.

Source: Calculated from Orphanet Journal of Rare Diseases (2024) (2024) - Diseases Getting First Effective Treatment Each Year

Uncertainty Range

Technical: 95% CI: [8 diseases/year, 30 diseases/year] • Distribution: Lognormal

What this means: This estimate is highly uncertain. The true value likely falls between 8 diseases/year and 30 diseases/year (±73%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Diseases Getting First Treatment Per Year

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

? Low confidence

NIH Standard Research Cost per QALY: $50K

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Typical cost per QALY for standard NIH-funded medical research portfolio. Reflects the inefficiency of traditional RCTs and basic research-heavy allocation. See confidence_interval for range; ICER uses higher thresholds for value-based pricing.

Source: PMC (1990) - Standard Medical Research ROI ($20k-$100k/QALY)

Uncertainty Range

Technical: 95% CI: [$20K, $100K] • Distribution: Lognormal

What this means: This estimate is highly uncertain. The true value likely falls between $20K and $100K (±80%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: NIH Standard Research Cost per QALY

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence

Pharma Drug Development Cost (Current System): $2.60B

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Average cost to develop one drug in current system

Source: Tufts CSDD - Cost of drug development

Uncertainty Range

Technical: 95% CI: [$1.50B, $4B] • Distribution: Lognormal (SE: $500M)

What this means: There’s significant uncertainty here. The true value likely falls between $1.50B and $4B (±48%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Pharma Drug Development Cost (Current System)

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence • 📊 Peer-reviewed

Phase I Safety Trial Duration: 2.3 years

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Phase I safety trial duration

Source: Biotechnology Innovation Organization (BIO) (2021) - BIO Clinical Development Success Rates 2011-2020

✓ High confidence • 📊 Peer-reviewed • Updated 2021

Pragmatic Trial Median Cost per Patient (PMC Review): $97

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Median cost per patient in embedded pragmatic clinical trials (systematic review of 64 trials). IQR: $19-$478 (2015 USD).

Source: PMC - Pragmatic Trial Cost per Patient (Median $97)

Uncertainty Range

Technical: 95% CI: [$19, $478] • Distribution: Lognormal

What this means: This estimate is highly uncertain. The true value likely falls between $19 and $478 (±237%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Pragmatic Trial Median Cost per Patient (PMC Review)

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Total Number of Rare Diseases Globally: 7.00k diseases

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Total number of rare diseases globally

Source: GAO (2025) - 95% of diseases have no effective treatment

Uncertainty Range

Technical: 95% CI: [6.00k diseases, 10.0k diseases] • Distribution: Normal

What this means: There’s significant uncertainty here. The true value likely falls between 6.00k diseases and 10.0k diseases (±29%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The normal distribution means values cluster around the center with equal chances of being higher or lower.

Input Distribution

Probability Distribution: Total Number of Rare Diseases Globally

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Recovery Trial Cost per Patient: $500

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RECOVERY trial cost per patient. Note: RECOVERY was an outlier - hospital-based during COVID emergency, minimal extra procedures, existing NHS infrastructure, streamlined consent. Replicating this globally will be harder.

Source: Oren Cass, Manhattan Institute (2023) - RECOVERY Trial Cost per Patient

Uncertainty Range

Technical: 95% CI: [$400, $2.50K] • Distribution: Lognormal

What this means: This estimate is highly uncertain. The true value likely falls between $400 and $2.50K (±210%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Recovery Trial Cost per Patient

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

RECOVERY Trial Global Lives Saved: 1.00M lives

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Estimated lives saved globally by RECOVERY trial’s dexamethasone discovery. NHS England estimate (March 2021). Based on Águas et al. Nature Communications 2021 methodology applying RECOVERY trial mortality reductions (36% ventilated, 18% oxygen) to global COVID hospitalizations. Wide uncertainty range reflects extrapolation assumptions.

Source: NHS England; Águas et al. (2021) - RECOVERY trial global lives saved (~1 million)

Uncertainty Range

Technical: 95% CI: [500k lives, 2.00M lives] • Distribution: Lognormal

What this means: This estimate is highly uncertain. The true value likely falls between 500k lives and 2.00M lives (±75%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: RECOVERY Trial Global Lives Saved

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence

RECOVERY Trial Total Cost: $20M

📊 Show full details

Total cost of UK RECOVERY trial. Enrolled tens of thousands of patients across multiple treatment arms. Discovered dexamethasone reduces COVID mortality by ~1/3 in severe cases.

Source: Manhattan Institute - RECOVERY trial 82× cost reduction

Uncertainty Range

Technical: 95% CI: [$15M, $25M] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between $15M and $25M (±25%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: RECOVERY Trial Total Cost

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Mean Age of Preventable Death from Post-Safety Efficacy Delay: 62 years

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Mean age of preventable death from post-safety efficacy testing regulatory delay (Phase 2-4)

Source: World Health Organization (2024) - WHO Global Health Estimates 2024

Uncertainty Range

Technical: Distribution: Normal (SE: 3 years)

Input Distribution

Probability Distribution: Mean Age of Preventable Death from Post-Safety Efficacy Delay

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence • 📊 Peer-reviewed

Pre-Death Suffering Period During Post-Safety Efficacy Delay: 6 years

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Pre-death suffering period during post-safety efficacy testing delay (average years lived with untreated condition while awaiting Phase 2-4 completion)

Source: World Health Organization (2024) - WHO Global Health Estimates 2024

Uncertainty Range

Technical: 95% CI: [4 years, 9 years] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between 4 years and 9 years (±42%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Pre-Death Suffering Period During Post-Safety Efficacy Delay

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence • 📊 Peer-reviewed

Return on Investment from Smallpox Eradication Campaign: 280:1

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Return on investment from smallpox eradication campaign

Source: CSIS - Smallpox Eradication ROI

✓ High confidence

Standard Economic Value per QALY: $150K

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Standard economic value per QALY

Source: ICER (2024) - Value per QALY (standard economic value)

Uncertainty Range

Technical: Distribution: Normal (SE: $30K)

Input Distribution

Probability Distribution: Standard Economic Value per QALY

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Thalidomide Cases Worldwide: 15.0k cases

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Total thalidomide birth defect cases worldwide (1957-1962)

Source: Wikipedia - Thalidomide scandal: worldwide cases and mortality

Uncertainty Range

Technical: 95% CI: [10.0k cases, 20.0k cases] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between 10.0k cases and 20.0k cases (±33%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Thalidomide Cases Worldwide

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence

Thalidomide Disability Weight: 0.4:1

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Disability weight for thalidomide survivors (limb deformities, organ damage)

Source: PLOS One (2019) - Health and quality of life of Thalidomide survivors as they age

Uncertainty Range

Technical: 95% CI: [0.32:1, 0.48:1] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between 0.32:1 and 0.48:1 (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Thalidomide Disability Weight

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence

Thalidomide Mortality Rate: 40%

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Mortality rate for thalidomide-affected infants (died within first year)

Source: Wikipedia - Thalidomide scandal: worldwide cases and mortality

Uncertainty Range

Technical: 95% CI: [35%, 45%] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between 35% and 45% (±13%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Thalidomide Mortality Rate

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Thalidomide Survivor Lifespan: 60 years

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Average lifespan for thalidomide survivors

Source: PLOS One (2019) - Health and quality of life of Thalidomide survivors as they age

Uncertainty Range

Technical: 95% CI: [50 years, 70 years] • Distribution: Lognormal

What this means: This estimate has moderate uncertainty. The true value likely falls between 50 years and 70 years (±17%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Thalidomide Survivor Lifespan

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

~ Medium confidence

US Population Share 1960: 6%

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US share of world population in 1960

Source: US Census Bureau - Historical world population estimates

Uncertainty Range

Technical: 95% CI: [5.5%, 6.5%] • Distribution: Lognormal

What this means: We’re quite confident in this estimate. The true value likely falls between 5.5% and 6.5% (±8%). This represents a narrow range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: US Population Share 1960

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Phase 3 Cost per Patient: $41K

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Phase 3 cost per patient (median from FDA study)

Source: FDA Study via NCBI - Trial Costs, FDA Study

Uncertainty Range

Technical: 95% CI: [$20K, $120K] • Distribution: Lognormal

What this means: This estimate is highly uncertain. The true value likely falls between $20K and $120K (±122%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Input Distribution

Probability Distribution: Phase 3 Cost per Patient

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Value of Statistical Life: $10M

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Value of Statistical Life (conservative estimate)

Source: DOT (2024) - DOT Value of Statistical Life ($13.6M)

Uncertainty Range

Technical: 95% CI: [$5M, $15M] • Distribution: Gamma (SE: $3M)

What this means: There’s significant uncertainty here. The true value likely falls between $5M and $15M (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The gamma distribution means values follow a specific statistical pattern.

Input Distribution

Probability Distribution: Value of Statistical Life

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

✓ High confidence

Core Definitions

Fundamental parameters and constants used throughout the analysis.

ADAPTABLE Trial Patients Enrolled: 15.1k patients

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Patients enrolled in ADAPTABLE trial (PCORnet 2016-2019). Enrolled across 40 clinical sites. Precise count from trial completion records.

Core definition

Mid-Range Funding for Commercial Dct Platform: $500M

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Mid-range funding for commercial DCT platform

Core definition

dFDA Direct Funding Cost per DALY: $0.841

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Cost per DALY if philanthropists/governments directly funded $21.76B/year for ~46.5 years (queue clearance period, NPV: ~$541.9B) instead of treaty campaign ($1B). Treaty achieves 542× leverage: $1B campaign unlocks government funding for 46.5 years (NPV: $541.9B), avoiding direct philanthropic commitment. Both achieve same 200B DALY timeline shift benefit. Still cost-effective vs bed nets ($89.0/DALY).

Core definition

dFDA Direct Funding NPV (Queue Clearance Period): $475B

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NPV of direct funding ($21.76B/year for medical research after bond/IAB allocations) for the ~46.5-year queue clearance period. Alternative scenario: instead of $1B treaty campaign to unlock government funding, philanthropists/NIH directly fund clinical trials until disease queue is cleared. Funding period is queue clearance time (46.5 years with 9.5× trial capacity), not timeline shift amount (207 years). After queue is cleared, the timeline shift benefit (200B DALYs) is fully realized.

Core definition

Decentralized Framework for Drug Assessment Core framework Annual OPEX: $18.9M

📊 Show full details

Decentralized Framework for Drug Assessment Core framework annual opex (midpoint of $11-26.5M)

Uncertainty Range

Technical: 95% CI: [$11M, $26.5M] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between $11M and $26.5M (±41%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Core definition

Decentralized Framework for Drug Assessment Core framework Build Cost: $40M

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Decentralized Framework for Drug Assessment Core framework build cost

Uncertainty Range

Technical: 95% CI: [$25M, $65M] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between $25M and $65M (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Core definition

Decentralized Framework for Drug Assessment Community Support Costs: $2M

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Decentralized Framework for Drug Assessment community support costs

Uncertainty Range

Technical: 95% CI: [$1M, $3M] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between $1M and $3M (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Core definition

Decentralized Framework for Drug Assessment Infrastructure Costs: $8M

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Decentralized Framework for Drug Assessment infrastructure costs (cloud, security)

Uncertainty Range

Technical: 95% CI: [$5M, $12M] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between $5M and $12M (±44%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Core definition

Decentralized Framework for Drug Assessment Maintenance Costs: $15M

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Decentralized Framework for Drug Assessment maintenance costs

Uncertainty Range

Technical: 95% CI: [$10M, $22M] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between $10M and $22M (±40%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Core definition

Decentralized Framework for Drug Assessment Regulatory Coordination Costs: $5M

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Decentralized Framework for Drug Assessment regulatory coordination costs

Uncertainty Range

Technical: 95% CI: [$3M, $8M] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between $3M and $8M (±50%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Core definition

Decentralized Framework for Drug Assessment Staff Costs: $10M

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Decentralized Framework for Drug Assessment staff costs (minimal, AI-assisted)

Uncertainty Range

Technical: 95% CI: [$7M, $15M] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between $7M and $15M (±40%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Core definition

Decentralized Framework for Drug Assessment One-Time Build Cost: $40M

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Decentralized Framework for Drug Assessment one-time build cost (central estimate)

Core definition

Decentralized Framework for Drug Assessment One-Time Build Cost (Maximum): $46M

📊 Show full details

Decentralized Framework for Drug Assessment one-time build cost (high estimate)

Core definition

DIH Broader Initiatives Annual OPEX: $21.1M

📊 Show full details

DIH broader initiatives annual opex (medium case)

Uncertainty Range

Technical: 95% CI: [$14M, $32M] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between $14M and $32M (±43%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Core definition

DIH Broader Initiatives Upfront Cost: $230M

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DIH broader initiatives upfront cost (medium case)

Uncertainty Range

Technical: 95% CI: [$150M, $350M] • Distribution: Lognormal

What this means: There’s significant uncertainty here. The true value likely falls between $150M and $350M (±44%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Core definition

Annual Funding for Pragmatic Clinical Trials: $21.8B

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Annual funding for pragmatic clinical trials (treaty funding minus VICTORY Incentive Alignment Bond payouts and IAB political incentive mechanism)

Core definition

Eventually Avoidable DALY Percentage: 92.6%

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Percentage of DALYs that are eventually avoidable with sufficient biomedical research. Uses same methodology as EVENTUALLY_AVOIDABLE_DEATH_PCT. Most non-fatal chronic conditions (arthritis, depression, chronic pain) are also addressable through research, so the percentage is similar to deaths.

Uncertainty Range

Technical: 95% CI: [50%, 98%] • Distribution: Beta

What this means: There’s significant uncertainty here. The true value likely falls between 50% and 98% (±26%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The beta distribution means values are bounded and can skew toward one end.

Core definition

Eventually Avoidable Death Percentage: 92.6%

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Percentage of deaths that are eventually avoidable with sufficient biomedical research and technological advancement. Central estimate ~92% based on ~7.9% fundamentally unavoidable (primarily accidents). Wide uncertainty reflects debate over: (1) aging as addressable vs. fundamental, (2) asymptotic difficulty of last diseases, (3) multifactorial disease complexity.

Uncertainty Range

Technical: 95% CI: [50%, 98%] • Distribution: Beta

The beta distribution means values are bounded and can skew toward one end.

Core definition

Annual IAB Political Incentive Funding: $2.72B

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Annual funding for IAB political incentive mechanism (independent expenditures supporting high-scoring politicians, post-office fellowship endowments, Public Good Score infrastructure)

Core definition

IAB Political Incentive Funding Percentage: 10%

📊 Show full details

Percentage of treaty funding allocated to Incentive Alignment Bond mechanism for political incentives (independent expenditures/PACs, post-office fellowships, Public Good Score infrastructure)

Uncertainty Range

Technical: Distribution: Fixed

Core definition

Standard Discount Rate for NPV Analysis: 3%

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Standard discount rate for NPV analysis (3% annual, social discount rate)

Uncertainty Range

Technical: Distribution: Fixed

Core definition

Standard Time Horizon for NPV Analysis: 10 years

📊 Show full details

Standard time horizon for NPV analysis

Uncertainty Range

Technical: Distribution: Fixed

Core definition

QALYs per COVID Death Averted: 5 QALYs/death

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Average QALYs gained per COVID death averted. Conservative estimate reflecting older age distribution of COVID mortality. See confidence_interval for range.

Uncertainty Range

Technical: 95% CI: [3 QALYs/death, 10 QALYs/death] • Distribution: Lognormal

What this means: This estimate is highly uncertain. The true value likely falls between 3 QALYs/death and 10 QALYs/death (±70%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Core definition

Safe Compounds Available for Testing: 9.50k compounds

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Total safe compounds available for repurposing (FDA-approved + GRAS substances, midpoint of 7,000-12,000 range)

Uncertainty Range

Technical: 95% CI: [7.00k compounds, 12.0k compounds] • Distribution: Uniform

What this means: There’s significant uncertainty here. The true value likely falls between 7.00k compounds and 12.0k compounds (±26%). This represents a wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The uniform distribution means any value in the range is equally likely.

Core definition

Tested Drug-Disease Relationships: 32.5k relationships

📊 Show full details

Estimated drug-disease relationships actually tested (approved uses + repurposed + failed trials, midpoint of 15,000-50,000 range)

Uncertainty Range

Technical: 95% CI: [15.0k relationships, 50.0k relationships] • Distribution: Lognormal

What this means: This estimate is highly uncertain. The true value likely falls between 15.0k relationships and 50.0k relationships (±54%). This represents a very wide range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The lognormal distribution means values can’t go negative and have a longer tail toward higher values (common for costs and populations).

Core definition

Annual Funding from 1% of Global Military Spending Redirected to DIH: $27.2B

📊 Show full details

Annual funding from 1% of global military spending redirected to DIH

Core definition

1% Reduction in Military Spending/War Costs from Treaty: 1%

📊 Show full details

1% reduction in military spending/war costs from treaty

Uncertainty Range

Technical: Distribution: Fixed

Core definition

Trial-Relevant Diseases: 1.00k diseases

📊 Show full details

Consolidated count of trial-relevant diseases worth targeting (after grouping ICD-10 codes)

Uncertainty Range

Technical: 95% CI: [800 diseases, 1.20k diseases] • Distribution: Uniform

What this means: This estimate has moderate uncertainty. The true value likely falls between 800 diseases and 1.20k diseases (±20%). This represents a reasonable range that our Monte Carlo simulations account for when calculating overall uncertainty in the results.

The uniform distribution means any value in the range is equally likely.

Core definition

Annual VICTORY Incentive Alignment Bond Payout: $2.72B

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Annual VICTORY Incentive Alignment Bond payout (treaty funding × bond percentage)

Core definition

Percentage of Captured Dividend Funding VICTORY Incentive Alignment Bonds: 10%

📊 Show full details

Percentage of captured dividend funding VICTORY Incentive Alignment Bonds (10%)

Uncertainty Range

Technical: Distribution: Fixed

Core definition

References

Fund, N. C. NIH pragmatic trials: Minimal funding despite 30x cost advantage. NIH Common Fund: HCS Research Collaboratory https://commonfund.nih.gov/hcscollaboratory (2025)

The NIH Pragmatic Trials Collaboratory funds trials at **$500K for planning phase, $1M/year for implementation**—a tiny fraction of NIH’s budget. The ADAPTABLE trial cost **$14 million** for **15,076 patients** (= **$929/patient**) versus **$420 million** for a similar traditional RCT (30x cheaper), yet pragmatic trials remain severely underfunded. PCORnet infrastructure enables real-world trials embedded in healthcare systems, but receives minimal support compared to basic research funding. Additional sources: https://commonfund.nih.gov/hcscollaboratory | https://pcornet.org/wp-content/uploads/2025/08/ADAPTABLE_Lay_Summary_21JUL2025.pdf | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604499/

NIH. Antidepressant clinical trial exclusion rates. Zimmerman et al. https://pubmed.ncbi.nlm.nih.gov/26276679/ (2015)

Mean exclusion rate: 86.1% across 158 antidepressant efficacy trials (range: 44.4% to 99.8%) More than 82% of real-world depression patients would be ineligible for antidepressant registration trials Exclusion rates increased over time: 91.4% (2010-2014) vs. 83.8% (1995-2009) Most common exclusions: comorbid psychiatric disorders, age restrictions, insufficient depression severity, medical conditions Emergency psychiatry patients: only 3.3% eligible (96.7% excluded) when applying 9 common exclusion criteria Only a minority of depressed patients seen in clinical practice are likely to be eligible for most AETs Note: Generalizability of antidepressant trials has decreased over time, with increasingly stringent exclusion criteria eliminating patients who would actually use the drugs in clinical practice Additional sources: https://pubmed.ncbi.nlm.nih.gov/26276679/ | https://pubmed.ncbi.nlm.nih.gov/26164052/ | https://www.wolterskluwer.com/en/news/antidepressant-trials-exclude-most-real-world-patients-with-depression

GiveWell. GiveWell cost per life saved for top charities (2024). GiveWell: Top Charities https://www.givewell.org/charities/top-charities

General range: $3,000-$5,500 per life saved (GiveWell top charities) Helen Keller International (Vitamin A): $3,500 average (2022-2024); varies $1,000-$8,500 by country Against Malaria Foundation: $5,500 per life saved New Incentives (vaccination incentives): $4,500 per life saved Malaria Consortium (seasonal malaria chemoprevention): $3,500 per life saved VAS program details: $2 to provide vitamin A supplements to child for one year Note: Figures accurate for 2024. Helen Keller VAS program has wide country variation ($1K-$8.5K) but $3,500 is accurate average. Among most cost-effective interventions globally Additional sources: https://www.givewell.org/charities/top-charities | https://www.givewell.org/charities/helen-keller-international | https://ourworldindata.org/cost-effectiveness

CAN, A. Clinical trial patient participation rate. ACS CAN: Barriers to Clinical Trial Enrollment https://www.fightcancer.org/policy-resources/barriers-patient-enrollment-therapeutic-clinical-trials-cancer

Only 3-5% of adult cancer patients in US receive treatment within clinical trials About 5% of American adults have ever participated in any clinical trial Oncology: 2-3% of all oncology patients participate Contrast: 50-60% enrollment for pediatric cancer trials (<15 years old) Note: 20% of cancer trials fail due to insufficient enrollment; 11% of research sites enroll zero patients Additional sources: https://www.fightcancer.org/policy-resources/barriers-patient-enrollment-therapeutic-clinical-trials-cancer | https://hints.cancer.gov/docs/Briefs/HINTS_Brief_48.pdf

ScienceDaily. Global prevalence of chronic disease. ScienceDaily: GBD 2015 Study https://www.sciencedaily.com/releases/2015/06/150608081753.htm (2015)

2.3 billion individuals had more than five ailments (2013) Chronic conditions caused 74% of all deaths worldwide (2019), up from 67% (2010) Approximately 1 in 3 adults suffer from multiple chronic conditions (MCCs) Risk factor exposures: 2B exposed to biomass fuel, 1B to air pollution, 1B smokers Projected economic cost: $47 trillion by 2030 Note: 2.3B with 5+ ailments is more accurate than "2B with chronic disease." One-third of all adults globally have multiple chronic conditions Additional sources: https://www.sciencedaily.com/releases/2015/06/150608081753.htm | https://pmc.ncbi.nlm.nih.gov/articles/PMC10830426/ | https://pmc.ncbi.nlm.nih.gov/articles/PMC6214883/

Report, I. Global trial capacity. IQVIA Report: Clinical Trial Subjects Number Drops Due to Decline in COVID-19 Enrollment https://gmdpacademy.org/news/iqvia-report-clinical-trial-subjects-number-drops-due-to-decline-in-covid-19-enrollment/

1.9M participants annually (2022, post-COVID normalization from 4M peak in 2021) Additional sources: https://gmdpacademy.org/news/iqvia-report-clinical-trial-subjects-number-drops-due-to-decline-in-covid-19-enrollment/

Medicine, N. Drug repurposing rate ( 30%). Nature Medicine https://www.nature.com/articles/s41591-024-03233-x (2024)

Approximately 30% of drugs gain at least one new indication after initial approval. Additional sources: https://www.nature.com/articles/s41591-024-03233-x

(BIO), B. I. O. BIO clinical development success rates 2011-2020. Biotechnology Innovation Organization (BIO) https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf (2021)

Phase I duration: 2.3 years average Total time to market (Phase I-III + approval): 10.5 years average Phase transition success rates: Phase I→II: 63.2%, Phase II→III: 30.7%, Phase III→Approval: 58.1% Overall probability of approval from Phase I: 12% Note: Largest publicly available study of clinical trial success rates. Efficacy lag = 10.5 - 2.3 = 8.2 years post-safety verification. Additional sources: https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf

size, D. from global market & ratios, public/private funding. Private industry clinical trial spending.

Private pharmaceutical and biotech industry spends approximately $75-90 billion annually on clinical trials, representing roughly 90% of global clinical trial spending.

10.

Organization, W. H. WHO global health estimates 2024. World Health Organization https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates (2024)

Comprehensive mortality and morbidity data by cause, age, sex, country, and year Global mortality: 55-60 million deaths annually Lives saved by modern medicine (vaccines, cardiovascular drugs, oncology): 12M annually (conservative aggregate) Leading causes of death: Cardiovascular disease (17.9M), Cancer (10.3M), Respiratory disease (4.0M) Note: Baseline data for regulatory mortality analysis. Conservative estimate of pharmaceutical impact based on WHO immunization data (4.5M/year from vaccines) + cardiovascular interventions (3.3M/year) + oncology (1.5M/year) + other therapies. Additional sources: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates

11.

PMC. Only 12% of human interactome targeted. PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC10749231/ (2023)

Mapping 350,000+ clinical trials showed that only 12% of the human interactome has ever been targeted by drugs. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10749231/

12.

Orphanet Journal of Rare Diseases (2024), C. from. Diseases getting first effective treatment each year. Calculated from Orphanet Journal of Rare Diseases (2024) https://ojrd.biomedcentral.com/articles/10.1186/s13023-024-03398-1 (2024)

Under the current system, approximately 10-15 diseases per year receive their FIRST effective treatment. Calculation: 5% of 7,000 rare diseases ( 350) have FDA-approved treatment, accumulated over 40 years of the Orphan Drug Act = 9 rare diseases/year. Adding 5-10 non-rare diseases that get first treatments yields 10-20 total. FDA approves 50 drugs/year, but many are for diseases that already have treatments (me-too drugs, second-line therapies). Only 15 represent truly FIRST treatments for previously untreatable conditions.

13.

PMC. Standard medical research ROI ($20k-$100k/QALY). PMC: Cost-effectiveness Thresholds Used by Study Authors https://pmc.ncbi.nlm.nih.gov/articles/PMC10114019/ (1990)

Typical cost-effectiveness thresholds for medical interventions in rich countries range from $50,000 to $150,000 per QALY. The Institute for Clinical and Economic Review (ICER) uses a $100,000-$150,000/QALY threshold for value-based pricing. Between 1990-2021, authors increasingly cited $100,000 (47% by 2020-21) or $150,000 (24% by 2020-21) per QALY as benchmarks for cost-effectiveness. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10114019/ | https://icer.org/our-approach/methods-process/cost-effectiveness-the-qaly-and-the-evlyg/

14.

CSDD, T. Cost of drug development.

Various estimates suggest $1.0 - $2.5 billion to bring a new drug from discovery through FDA approval, spread across 10 years. Tufts Center for the Study of Drug Development often cited for $1.0 - $2.6 billion/drug. Industry reports (IQVIA, Deloitte) also highlight $2+ billion figures.

15.

PMC. PMC: Costs of Pragmatic Clinical Trials https://pmc.ncbi.nlm.nih.gov/articles/PMC6508852/

The median cost per participant was $97 (IQR $19–$478), based on 2015 dollars. Systematic review of 64 embedded pragmatic clinical trials. 25% of trials cost <$19/patient; 10 trials exceeded $1,000/patient. U.S. studies median $187 vs non-U.S. median $27. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC6508852/

16.

GAO. 95% of diseases have no effective treatment. GAO https://www.gao.gov/products/gao-25-106774 (2025)

95% of diseases have no treatment Additional sources: https://www.gao.gov/products/gao-25-106774 | https://globalgenes.org/rare-disease-facts/

17.

Oren Cass, M. I. RECOVERY trial cost per patient. Oren Cass https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs (2023)

The RECOVERY trial, for example, cost only about
$500 per patient... By contrast, the median per-patient cost of a pivotal trial for a new therapeutic is around \\$41,000. Additional sources: https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs

18.

al., N. E. Á. et. RECOVERY trial global lives saved ( 1 million). NHS England: 1 Million Lives Saved https://www.england.nhs.uk/2021/03/covid-treatment-developed-in-the-nhs-saves-a-million-lives/ (2021)

Dexamethasone saved 1 million lives worldwide (NHS England estimate, March 2021, 9 months after discovery). UK alone: 22,000 lives saved. Methodology: Águas et al. Nature Communications 2021 estimated 650,000 lives (range: 240,000-1,400,000) for July-December 2020 alone, based on RECOVERY trial mortality reductions (36% for ventilated, 18% for oxygen-only patients) applied to global COVID hospitalizations. June 2020 announcement: Dexamethasone reduced deaths by up to 1/3 (ventilated patients), 1/5 (oxygen patients). Impact immediate: Adopted into standard care globally within hours of announcement. Additional sources: https://www.england.nhs.uk/2021/03/covid-treatment-developed-in-the-nhs-saves-a-million-lives/ | https://www.nature.com/articles/s41467-021-21134-2 | https://pharmaceutical-journal.com/article/news/steroid-has-saved-the-lives-of-one-million-covid-19-patients-worldwide-figures-show | https://www.recoverytrial.net/news/recovery-trial-celebrates-two-year-anniversary-of-life-saving-dexamethasone-result

19.

Institute, M. RECOVERY trial 82× cost reduction. Manhattan Institute: Slow Costly Trials https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs

RECOVERY trial: $500 per patient ($20M for 48,000 patients = $417/patient) Typical clinical trial: $41,000 median per-patient cost Cost reduction: 80-82× cheaper ($41,000 ÷ $500 ≈ 82×) Efficiency: $50 per patient per answer (10 therapeutics tested, 4 effective) Dexamethasone estimated to save >630,000 lives Additional sources: https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs | https://pmc.ncbi.nlm.nih.gov/articles/PMC9293394/

20.

CSIS. Smallpox eradication ROI. CSIS https://www.csis.org/analysis/smallpox-eradication-model-global-cooperation.

21.

ICER. Value per QALY (standard economic value). ICER https://icer.org/wp-content/uploads/2024/02/Reference-Case-4.3.25.pdf (2024)

Standard economic value per QALY: $100,000–$150,000. This is the US and global standard willingness-to-pay threshold for interventions that add costs. Dominant interventions (those that save money while improving health) are favorable regardless of this threshold. Additional sources: https://icer.org/wp-content/uploads/2024/02/Reference-Case-4.3.25.pdf

22.

Wikipedia. Thalidomide scandal: Worldwide cases and mortality. Wikipedia https://en.wikipedia.org/wiki/Thalidomide_scandal

The total number of embryos affected by the use of thalidomide during pregnancy is estimated at 10,000, of whom about 40% died around the time of birth. More than 10,000 children in 46 countries were born with deformities such as phocomelia. Additional sources: https://en.wikipedia.org/wiki/Thalidomide_scandal

23.

NCBI, F. S. via. Trial costs, FDA study. FDA Study via NCBI https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248200/

Overall, the 138 clinical trials had an estimated median (IQR) cost of
$19.0 million (\\$12.2 million-
$33.1 million)... The clinical trials cost a median (IQR) of \\$41,117 (
$31,802-\\$82,362) per patient. Additional sources: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248200/

24.

DOT. DOT value of statistical life ($13.6M). DOT: VSL Guidance 2024 https://www.transportation.gov/office-policy/transportation-policy/revised-departmental-guidance-on-valuation-of-a-statistical-life-in-economic-analysis (2024)

Current VSL (2024): $13.7 million (updated from $13.6M) Used in cost-benefit analyses for transportation regulations and infrastructure Methodology updated in 2013 guidance, adjusted annually for inflation and real income VSL represents aggregate willingness to pay for safety improvements that reduce fatalities by one Note: DOT has published VSL guidance periodically since 1993. Current $13.7M reflects 2024 inflation/income adjustments Additional sources: https://www.transportation.gov/office-policy/transportation-policy/revised-departmental-guidance-on-valuation-of-a-statistical-life-in-economic-analysis | https://www.transportation.gov/regulations/economic-values-used-in-analysis

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Overview

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Calculated Values

Combination Therapy Space: 45.1B combinations

Sensitivity Analysis

Pairwise Drug Combinations: 45.1M combinations

Sensitivity Analysis

Combination Therapy Exploration Time (Current): 13.7M years

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Known Safe Exploration Time (Current): 2.88k years

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Total Annual Decentralized Framework for Drug Assessment Operational Costs: $40M

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Decentralized Framework for Drug Assessment Annual Benefit: R&D Savings: $58.6B

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Total DALYs Lost from Disease Eradication Delay: 7.94B DALYs

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Total Deaths from Disease Eradication Delay: 416M deaths

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Total Economic Loss from Disease Eradication Delay: $1.19 quadrillion

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Years Lived with Disability During Disease Eradication Delay: 873M years

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Years of Life Lost from Disease Eradication Delay: 7.07B years

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

dFDA New Treatments Per Year: 185 diseases/year

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Decentralized Framework for Drug Assessment Annual Net Savings (R&D Only): $58.6B

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Decentralized Framework for Drug Assessment Total NPV Annual OPEX: $40M

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

NPV of Decentralized Framework for Drug Assessment Benefits (R&D Only, 10-Year Discounted): $389B

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

NPV Net Benefit (R&D Only): $389B

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Decentralized Framework for Drug Assessment Present Value of Annual OPEX Over 10 Years: $342M

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Decentralized Framework for Drug Assessment Total NPV Cost: $611M

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Decentralized Framework for Drug Assessment Total NPV Upfront Costs: $270M

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

dFDA Queue Clearance Time: 36 years

Sensitivity Analysis

Monte Carlo Distribution

Exceedance Probability

Daily R&D Savings from Trial Cost Reduction: $161M