Permutation tests for detecting treatment effect heterogeneity in cluster randomized trials
Bibliographic record
Abstract
Cluster randomized trials are widely used in healthcare research for the evaluation of intervention strategies. Beyond estimating the average treatment effect, it is often of interest to assess whether the treatment effect varies across subgroups. While conventional methods based on tests of interaction terms between treatment and covariates can be used to detect treatment effect heterogeneity in cluster randomized trials, they typically rely on parametric assumptions that may not hold in practice. Adapting existing permutation tests from individually randomized trials, however, requires conceptual clarification and modification due to the multiple possible interpretations of treatment effect heterogeneity in the cluster randomized trial context. In this work, we develop variations of permutation tests and clarify key causal definitions in order to assess treatment effect heterogeneity in cluster randomized trials. Our procedure enables investigators to simultaneously test for effect modification across a large number of covariates, while maintaining nominal type I error rates and reasonable power in simulation studies. In the Pain Program for Active Coping and Training (PPACT) study, the proposed methods are able to detect treatment effect heterogeneity that was not identified by conventional methods assessing treatment-covariate interactions.
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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 | 0.140 | 0.803 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".