Evidence-Based Policy Making for Public Health Interventions in Cardiovascular Diseases: Formally Assessing the Feasibility of Clinical Trials
Bibliographic record
Abstract
Implementation of prevention policies has often been impeded or delayed due to the lack of randomized controlled trials (RCTs) with hard clinical outcomes (eg, incident disease, mortality). Despite the prominent role of RCTs in health care, it may not always be feasible to conduct RCTs of public health interventions with hard outcomes due to logistical and ethical considerations. RCTs may also lack external validity and have limited generalizability. Currently, there is insufficient guidance for policymakers charged with establishing evidence-based policy to determine whether an RCT with hard outcomes is needed before policy recommendations. In this context, the purpose of this article is to assess, in a case study, the feasibility of conducting an RCT of the oft-cited issue of sodium reduction on cardiovascular outcomes and then propose a framework for decision-making, which includes an assessment of the feasibility of conducting an RCT with hard clinical outcomes when such trials are unavailable. We designed and assessed the feasibility of potential individual- and cluster-randomized trials of sodium reduction on cardiovascular outcomes. Based on our assumptions, a trial using any of the designs considered would require tens of thousands of participants and cost hundreds of millions of dollars, which is prohibitively expensive. Our estimates may be conservative given several key challenges, such as the unknown costs of sustaining a long-term difference in sodium intake, the effect of differential cotreatment with antihypertensive medications, and long lag time to clinical outcomes. Thus, it would be extraordinarily difficult to conduct such a trial, and despite the high costs, would still be at substantial risk for a spuriously null result. A robust framework, such as the one we developed, should be used to guide policymakers when establishing evidence-based public health interventions in the absence of trials with hard clinical outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.915 | 0.959 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.037 | 0.040 |
| Open science | 0.014 | 0.020 |
| Research integrity | 0.037 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".