Evidence-based Urology: Subgroup Analysis in Randomized Controlled Trials
Bibliographic record
Abstract
In randomized controlled trials, investigators often explore the possibility that the treatment effects differ between subgroups (eg, women vs men, old vs young, more versus less severe disease). Investigators often inappropriately claim subgroup effects (also called "effect modification" or "interaction") when the likelihood of a true effect modification is low. Criteria for assessing the credibility of subgroup analyses, nicely summarized in a formal Instrument for Assessing the Credibility of Effect Modification Analyses (ICEMAN), include investigator postulation of a priori hypotheses with a specified direction; support from prior evidence; a low likelihood that chance explains the apparent subgroup effect; and only testing a small number of subgroup hypotheses. PATIENT SUMMARY: Randomized clinical trials often use subgroup analyses to explore whether a treatment is more or less effective in a particular patient subgroup (eg, women vs men, old vs young). In this mini-review, we explore the common pitfalls of subgroup analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.234 | 0.889 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.074 | 0.018 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; both teacher heads 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".