Pragmatic Clinical Trial Funding Reform
Grade A — Strong EvidenceRedirect research funding to pragmatic real-world trials. UK NIHR produces actionable evidence at 1/10th the cost.
Rank #2 of 22 policies
📊 Bradford Hill Criteria Scores
💥 Impact Breakdown
🧪 Natural Experiments
Real-world before/after data from jurisdictions that implemented this policy.
Singapore — Universal Healthcare with Market Competition (3M System)
Intervention year: 1984 · Medisave (1984), MediShield (1990), Medifund (1993) — universal coverage with individual responsibility and competition
🌍 Health Systems by Country
How countries compare on this policy domain. The US row is highlighted.
| Country | $/Capita | Life Exp. | Infant Mort. | System |
|---|---|---|---|---|
| Japan | $4,691 | 84.8 | 1.8 | bismarck |
| Singapore | $3,013 | 84.1 | 1.7 | mixed |
| South Korea | $3,914 | 83.7 | 2.7 | bismarck |
| Switzerland | $9,044 | 83.4 | 3.3 | bismarck |
| Norway | $8,093 | 83.3 | 1.8 | beveridge |
| Sweden | $6,262 | 83.2 | 2.1 | beveridge |
| Australia | $5,627 | 83.0 | 3.1 | mixed |
| Israel | $3,266 | 82.6 | 2.8 | mixed |
| France | $5,564 | 82.5 | 3.2 | bismarck |
| Canada | $5,905 | 81.7 | 4.3 | single-payer |
| Netherlands | $6,190 | 81.5 | 3.1 | bismarck |
| Taiwan | $3,295 | 80.9 | 4.0 | single-payer |
| United Kingdom | $5,138 | 80.7 | 3.4 | beveridge |
| Germany | $7,383 | 80.6 | 3.1 | bismarck |
| Costa Rica | $1,285 | 80.3 | 7.0 | mixed |
| Thailand | $812 | 78.7 | 7.1 | mixed |
| Cuba | $966 | 78.0 | 4.0 | beveridge |
| United States | $12,555 | 77.5 | 5.4 | private |
| Brazil | $1,321 | 75.3 | 12.4 | mixed |
| India | $231 | 70.8 | 25.5 | mixed |
📋 Policy Details
NIH spends $48B/yr but 70%+ goes to indirect costs. 85% of findings fail to replicate. UK NIHR model: pragmatic trials embedded in NHS produce actionable evidence at 1/10th the cost. PCORI pragmatic trials show 3x faster clinical adoption.
🔬 Evidence Assessment: Bradford Hill Criteria
The Bradford Hill criteria are nine principles used to establish evidence of a causal relationship between a policy intervention and its outcomes. Originally developed for epidemiology (1965), they provide a structured framework for evaluating whether an observed association is truly causal. Each criterion is scored from 0 to 1.
How large is the association between the policy and the outcome? Larger effect sizes increase confidence in causation.
Has the relationship been observed across different populations, settings, and times? Replication strengthens causal claims.
Does the policy change precede the outcome change? Temporal ordering is a necessary condition for causation.
Is there a dose-response relationship? More of the policy leads to more of the effect? Gradients support causation.
Is there evidence from randomized controlled trials or natural experiments? Experimental evidence is the gold standard.
Is there a plausible mechanism explaining how the policy causes the outcome? Mechanistic understanding increases confidence.
Does the causal interpretation fit with existing knowledge? The relationship should not contradict established facts.
Are there analogous policies that have produced similar effects? Similar interventions with known effects support the claim.
Is the effect specific to this policy rather than a general phenomenon? Specific associations are more likely causal.
How is the Causal Confidence Score calculated?
The Causal Confidence Score (CCS) of 80% is a weighted average of the nine Bradford Hill criteria. Experiment and temporality receive higher weights since they provide the strongest evidence for causation. The CCS is then combined with the estimated effect magnitude to produce the Policy Impact Score (PIS) of 64%.
See the Optimal Policy Generator paper for full methodology.
Analysis: · Optimitron OPG