期刊
STATISTICS IN MEDICINE
卷 33, 期 20, 页码 3556-3575出版社
WILEY
DOI: 10.1002/sim.6185
关键词
cluster randomized trial; binary outcomes; Monte Carlo simulations; selection bias; propensity score
类别
资金
- French Agence Nationale de la Recherche [ANR-2010-PRSP-010-01]
Despite randomization, selection bias may occur in cluster randomized trials. Classical multivariable regression usually allows for adjusting treatment effect estimates with unbalanced covariates. However, for binary outcomes with low incidence, such a method may fail because of separation problems. This simulation study focused on the performance of propensity score (PS)-based methods to estimate relative risks from cluster randomized trials with binary outcomes with low incidence. The results suggested that among the different approaches used (multivariable regression, direct adjustment on PS, inverse weighting on PS, and stratification on PS), only direct adjustment on the PS fully corrected the bias and moreover had the best statistical properties. Copyright (C) 2014 John Wiley & Sons, Ltd.
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