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Methodology, Parameters, and Calculations
Parameter definitions, formulas, uncertainty ranges, and data sources.
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 34 parameters used in the analysis, organized by type:
- External sources (peer-reviewed): 9
- Calculated values: 12
- Core definitions: 13
Calculated Values
Parameters derived from mathematical formulas and economic models.
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
β 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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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.
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 π: 1.90M patients/year (95% CI: 1.50M patients/year - 2.30M patients/year)
- Patients Fundable Annually π’: 23.4M patients/year
\[ \begin{gathered} k_{capacity} = \frac{N_{fundable,ann}}{Slots_{curr}} = \frac{23.4M}{1.9M} = 12.3 \\[0.5em] \text{where } N_{fundable,ann} \\ = \frac{Subsidies_{trial,ann}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.7B}{\$929} \\ = 23.4M \\[0.5em] \text{where } Subsidies_{trial,ann} \\ = Treasury_{RD,ann} - OPEX_{dFDA} \\ = \$21.8B - \$40M \\ = \$21.7B \\[0.5em] \text{where } OPEX_{dFDA} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \\[0.5em] \text{where } Treasury_{RD,ann} \\ = Funding_{treaty} - Payout_{bond,ann} - Funding_{political,ann} \\ = \$27.2B - \$2.72B - \$2.72B \\ = \$21.8B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Payout_{bond,ann} \\ = Funding_{treaty} \times Pct_{bond} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \\[0.5em] \text{where } Funding_{political,ann} \\ = Funding_{treaty} \times Pct_{political} \\ = \$27.2B \times 10\% \\ = \$2.72B \\[0.5em] \text{where } Funding_{treaty} \\ = Spending_{mil} \times Reduce_{treaty} \\ = \$2.72T \times 1\% \\ = \$27.2B \end{gathered} \]
β 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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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
Noteπ Show full details
Total DALYs averted from the combined dFDA timeline shift. Calculated as annual global DALY burden Γ eventually avoidable percentage Γ timeline shift years. Includes both fatal and non-fatal diseases (WHO GBD methodology).
Inputs:
- Global Annual DALY Burden π: 2.88B DALYs/year (SE: Β±150M DALYs/year)
- Eventually Avoidable DALY Percentage: 92.6% (95% CI: 50% - 98%)
- dFDA Average Total Timeline Shift π’: 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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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
Noteπ Show full details
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 π’: 565B DALYs
- Standard Economic Value per QALY π: $150K (SE: Β±$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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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 π: 150k deaths/day (SE: Β±7.50k deaths/day)
- dFDA Average Total Timeline Shift π’: 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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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.
dFDA Average Total Timeline Shift: 212 years
Noteπ Show full details
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 π’: 204 years
- Regulatory Delay for Efficacy Testing Post-Safety Verification π: 8.2 years (SE: Β±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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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
Noteπ Show full details
Years earlier the average first treatment arrives due to dFDAβs 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 donβt directly speed queue processing.
Inputs:
- Status Quo Average Years to First Treatment π’: 222 years
- Trial Capacity Multiplier π’: 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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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.
Patients Fundable Annually: 23.4M patients/year
Noteπ Show full details
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 π’: $21.7B
- dFDA Pragmatic Trial Cost per Patient π: $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
β 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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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
Noteπ Show full details
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 π’: $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
β 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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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
Noteπ Show full details
Number of diseases without effective treatment. 95% of 7,000 rare diseases lack FDA-approved treatment (per Orphanet 2024). This is the βqueueβ of diseases waiting for cures.
Inputs:
- Total Number of Rare Diseases Globally π: 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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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.
Status Quo Average Years to First Treatment: 222 years
Noteπ Show full details
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 π’: 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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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
Noteπ Show full details
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 π’: 6.65k diseases
- Diseases Getting First Treatment Per Year π: 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 |
Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near Β±1 indicate strong influence; values exceeding Β±1 may occur with correlated inputs.
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
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.
External Data Sources
Parameters sourced from peer-reviewed publications, institutional databases, and authoritative reports.
Annual Global Clinical Trial Participants: 1.90M patients/year
Noteπ Show full details
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] β’ 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 (Β±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 canβt go negative and have a longer tail toward higher values (common for costs and populations).
Input Distribution
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.
β High confidence
dFDA Pragmatic Trial Cost per Patient: $929
Noteπ Show full details
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] β’ Distribution: Lognormal
What this means: This estimate is highly uncertain. The true value likely falls between $97 and $3K (Β±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 canβt go negative and have a longer tail toward higher values (common for costs and populations).
Input Distribution
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
Regulatory Delay for Efficacy Testing Post-Safety Verification: 8.2 years
Noteπ Show full details
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.
Uncertainty Range
Technical: Distribution: Normal (SE: 2 years)
Input Distribution
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 β’ π Peer-reviewed β’ Updated 2021
Global Annual DALY Burden: 2.88B DALYs/year
Noteπ Show full details
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.
Uncertainty Range
Technical: Distribution: Normal (SE: 150M DALYs/year)
Input Distribution
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.
β High confidence β’ π Peer-reviewed
Global Daily Deaths from Disease and Aging: 150k deaths/day
Noteπ Show full details
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
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.
β High confidence β’ π Peer-reviewed
Global Military Spending in 2024: $2.72T
Noteπ Show full details
Global military spending in 2024
Source: SIPRI (2025) - Global military spending ($2.72T, 2024)
Uncertainty Range
Technical: Distribution: Fixed
β High confidence
Diseases Getting First Treatment Per Year: 15 diseases/year
Noteπ Show full details
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 Γ· 40 years), plus ~5-10 common diseases. Note: FDA approves ~50 drugs/year, but most are for diseases that already have treatments.
Uncertainty Range
Technical: 95% CI: [8 diseases/year, 30 diseases/year] β’ Distribution: Lognormal
What this means: This estimate is highly uncertain. The true value likely falls between 8 diseases/year and 30 diseases/year (Β±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 canβt go negative and have a longer tail toward higher values (common for costs and populations).
Input Distribution
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
Total Number of Rare Diseases Globally: 7.00k diseases
Noteπ Show full details
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] β’ Distribution: Normal
What this means: Thereβs significant uncertainty here. The true value likely falls between 6.00k diseases and 10.0k diseases (Β±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
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.
β High confidence
Standard Economic Value per QALY: $150K
Noteπ Show full details
Standard economic value per QALY
Source: ICER (2024) - Value per QALY (standard economic value)
Uncertainty Range
Technical: Distribution: Normal (SE: $30K)
Input Distribution
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.
β High confidence
Core Definitions
Fundamental parameters and constants used throughout the analysis.
Decentralized Framework for Drug Assessment Community Support Costs: $2M
Noteπ Show full details
Decentralized Framework for Drug Assessment community support costs
Uncertainty Range
Technical: 95% CI: [$1M, $3M] β’ Distribution: Lognormal
What this means: Thereβs significant uncertainty here. The true value likely falls between $1M and $3M (Β±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 canβt 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
Noteπ Show full details
Decentralized Framework for Drug Assessment infrastructure costs (cloud, security)
Uncertainty Range
Technical: 95% CI: [$5M, $12M] β’ Distribution: Lognormal
What this means: Thereβs significant uncertainty here. The true value likely falls between $5M and $12M (Β±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 canβt 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
Noteπ Show full details
Decentralized Framework for Drug Assessment maintenance costs
Uncertainty Range
Technical: 95% CI: [$10M, $22M] β’ Distribution: Lognormal
What this means: Thereβs significant uncertainty here. The true value likely falls between $10M and $22M (Β±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 canβt 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
Noteπ Show full details
Decentralized Framework for Drug Assessment regulatory coordination costs
Uncertainty Range
Technical: 95% CI: [$3M, $8M] β’ Distribution: Lognormal
What this means: Thereβs significant uncertainty here. The true value likely falls between $3M and $8M (Β±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 canβt 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
Noteπ Show full details
Decentralized Framework for Drug Assessment staff costs (minimal, AI-assisted)
Uncertainty Range
Technical: 95% CI: [$7M, $15M] β’ Distribution: Lognormal
What this means: Thereβs significant uncertainty here. The true value likely falls between $7M and $15M (Β±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 canβt 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
Noteπ Show full details
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%
Noteπ Show full details
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%] β’ Distribution: Beta
What this means: Thereβs significant uncertainty here. The true value likely falls between 50% and 98% (Β±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
Annual IAB Political Incentive Funding: $2.72B
Noteπ Show full details
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%
Noteπ 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
Annual Funding from 1% of Global Military Spending Redirected to DIH: $27.2B
Noteπ 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%
Noteπ Show full details
1% reduction in military spending/war costs from treaty
Uncertainty Range
Technical: Distribution: Fixed
Core definition
Annual VICTORY Incentive Alignment Bond Payout: $2.72B
Noteπ Show full details
Annual VICTORY Incentive Alignment Bond payout (treaty funding Γ bond percentage)
Core definition
Percentage of Captured Dividend Funding VICTORY Incentive Alignment Bonds: 10%
Noteπ Show full details
Percentage of captured dividend funding VICTORY Incentive Alignment Bonds (10%)
Uncertainty Range
Technical: Distribution: Fixed
Core definition
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Β© 2025 Mike P. Sinn