Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Artículo Open Access |
| Publié: |
Wiley
2026
|
| Sujets: | |
| Accès en ligne: | https://onlinelibrary.wiley.com/doi/10.1002/sim.70408 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Table des matières:
- A Saddlepoint Framework for Accurate Inference in Multicenter Clinical Trials With Imbalanced Clusters Haidy A. Newer Statistics in Medicine ABSTRACT Statistical inference in multicenter clinical trials is often compromised when relying on asymptotic normal approximations, particularly in designs characterized by a small number of centers or severe imbalance in patient enrollment. Such deviations from asymptotic assumptions frequently result in unreliable p ‐values and a breakdown of error control. To resolve this, we introduce a high‐precision saddlepoint approximation framework for aggregate permutation tests within hierarchically structured data. The theoretical core of our approach is the derivation of a multilevel nested cumulant generating function that explicitly models the trial hierarchy, analytically integrating patient‐level linear rank statistics with the stochastic aggregation process across centers. A significant innovation of this work is the extension to the bivariate setting to address co‐primary endpoints , providing a robust inferential solution for mixed continuous (efficacy) and discrete (safety) outcomes where standard multivariate normality is unattainable. The resulting framework yields simulation‐free, highly accurate tail probabilities even in finite‐sample regimes. Extensive simulation studies confirm that our method maintains strict Type I error control in scenarios where asymptotic methods exhibit substantial inflation. Furthermore, an application to the multicenter diabetes prevention program trial demonstrates the method's practical utility: it correctly identifies a significant cardiovascular risk factor that standard approximations failed to detect, thereby preventing a critical Type II error and ensuring valid clinical conclusions. 10.1002/sim.70408 http://onlinelibrary.wiley.com/termsAndConditions#vor