Sample size considerations to assess sex-related treatment effects
Bibliographic record
Abstract
Randomized controlled trials (RCTs) represent the gold standard for establishing the efficacy of new interventions. Yet, RCTs are often conducted in selected populations, not representative of those at risk for or living with the disease. This lack of representativeness has raised concerns about the generalizability of RCT results to those populations not (adequately) represented in RCTs, in particular in the absence of evidence on potential differences in treatment effects across population subgroups. In the field of cardiovascular disease, women are often underrepresented in RCTs relative to disease burden. The present paper seeks to define representative participation of women in RCTs. Trial data from two cardiovascular trials, the ADVANCE trial and the LoDoCo2 trial, are used to illustrate sample size requirements for (i) achieving representative participation, (ii) achieving representative contribution to knowledge gained, and (iii) assessing whether treatment effects are the same or different in men and women (i.e. sex * treatment interactions). Achieving representative participation and representative contribution to knowledge gained requires relatively small increases in sample size and is mainly driven by sex differences in disease rates. However, sample size requirements for RCTs powered to assess sex differences in treatment effects are unfeasibly large. Hence, while efforts to enhance representative trial participation should continue, as RCTs provide the cornerstone for guideline recommendations and clinical decision-making, these should be complemented by mechanistic studies to provide insights in possible treatment interactions and inform optimal treatment for both women and men.
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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 | 0.399 | 0.654 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".