Performance of Principal Stratification Method Adjusting for Treatment Noncompliance in Two Arms of a Randomized Trial
Bibliographic record
Abstract
The method of principal stratification is a unifying framework for modelling cause and effect which is applicable to adjusting for treatment noncompliance in multiple arms of a trial. Baseline covariates which predict compliance with treatment are useful in addressing parameter identification problem associated with principal stratification. Roy, Hogan and Marcus (RHM) (2008) proposed a principal stratification framework in which they used baseline covariates to adjust for imperfect compliance in both arms of a two-active treatments trial. Key to the application of this method is a defining but untestable distributional assumption whose robustness is unknown. The present work uses statistically designed simulation studies in the framework of a clinical trial comparing two active treatments as applied to survival data under both homogeneous and heterogeneous treatment effect assumptions to evaluate the performance of the RHM method in terms of bias and $95\%$ credible intervals. We first apply the standard proportional hazard model to obtain the ITT estimate and evaluate resulting bias if viewed as estimating a causal hazard ratio. We then compare the method's performance in terms of stratum-specific causal relative risk for different specifications of a user-defined spectrum parameter. The results showed no effect of the spectrum parameter on the ITT estimates. The RHM method performed poorly by producing significantly biased efficacy estimates in all strata with wider corresponding $95\%$ credible intervals under heterogeneous treatment effect assumption. The resulting efficacy estimates varied a lot depending on the value of the unknown (user-defined) spectrum parameter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.198 | 0.325 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".