Properties of inverse probability of adherence weighted estimator of the per-protocol effect for sustained treatment strategies under different data-generating mechanisms and adherence patterns
Bibliographic record
Abstract
Inverse Probability (of Adherence) Weighted per-protocol (IPW-PP) estimators are get- ting popular in addressing medication non-adherence while analyzing pragmatic trial data. However, their finite sample properties under different data generating mechanisms (DGMs) have not been investigated comprehensively. In the current work, we investigated the finite sample performances of such estimators in the context of a pragmatic random- ized controlled trial. We compared the performances of IPW-PP estimators with commonly used naive and baseline-adjusted per-protocol estimators, under different DGMs emulating pragmatic trials, comparing two sustained treatment strategies, possibly with a non-null effect. DGMs include (i) different roles of a baseline variable; whether future time-varying prognostic factors are impacted by past adherence; and whether the baseline variable is measured, (ii) whether adherence patterns observed in two arms are differential, and when we have access to measurements of adherence and confounders that are recorded infre- quently (sparsely). When baseline confounders are adjusted, we generally obtain unbiased estimates, but if some necessary variables are not measured, the IPW-PP estimator may still be preferable. High non-adherence patterns might negatively impact IPW-PP effect estimators, particularly when DGMs include confounding that may be influenced by previ- ous adherence history. We used the above estimators to analyze a case study from the Lipid Research Clinics Coronary Primary Prevention Trial data in the presence of non-adherence. Journal of Statistical Research 2022, Vol. 56, No. 2, pp.134-154
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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 | 0.238 | 0.590 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".