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Abstract
Combining patient-level data from clinical trials can connect rare phenomena with clinical endpoints, but statistical techniques applied to a single trial may become problematical when trials are pooled. Estimating the hazard of a binary variable unevenly distributed across trials showcases a common pooled database issue. We studied how an unevenly distributed binary variable can compromise the integrity of fixed and random effects Cox proportional hazards (cph) models. We compared fixed effect and random effects cph models on a set of simulated datasets inspired by a 17-trial pooled database of patients presenting with ST segment elevation myocardial infarction (STEMI) and non-STEMI undergoing percutaneous coronary intervention. An unevenly distributed covariate can bias hazard ratio estimates, inflate standard errors, raise type I error, and reduce power. While uneveness causes problems for all cph models, random effects suffer least. Compared to fixed effect models, random effects suffer lower bias and trade inflated type I errors for improved power. Contrasting hazard rates between trials prevent accurate estimates from both fixed and random effects models.
Citation
McAndrew, Thomas, et al. “How Cox models react to a study-specific confounder in a patient-level pooled dataset: random effects better cope with an imbalanced covariate across trials unless baseline hazards differ.” Journal of Applied Statistics 46.10 (2019): 1903-1916.
@article{mcandrew2019cox,
title={How Cox models react to a study-specific confounder in a patient-level pooled dataset: random effects better cope with an imbalanced covariate across trials unless baseline hazards differ},
author={McAndrew, Thomas and Redfors, Bjorn and Crowley, Aaron and Zhang, Yiran and Chen, Shmuel and Golomb, Mordechai and Alu, Maria C and Francese, Dominic P and Ben-Yehuda, Ori and Maehara, Akiko and others},
journal={Journal of Applied Statistics},
volume={46},
number={10},
pages={1903--1916},
year={2019},
publisher={Taylor \& Francis}
}