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Estimating efficacy in clinical trials with clustered binary responses
Author(s) -
Albert Jeffrey M.
Publication year - 2002
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1059
Subject(s) - statistics , cluster analysis , causal inference , econometrics , cluster (spacecraft) , randomized controlled trial , sample size determination , binary data , clinical trial , binary number , computer science , mathematics , medicine , arithmetic , programming language
When non‐compliance occurs in a clinical trial, it may be of interest to supplement the intent‐to‐treat analysis with an analysis of the efficacy (or biological effect) of therapy. Sommer and Zeger (1991) developed a method for estimating efficacy applicable to the case of a binary response variable and all‐or‐none compliance that assumes independent subject responses. We extend this approach to accommodate within‐cluster correlations as may be expected in a cluster‐randomized design. The method is illustrated using data from a controlled village‐randomized clinical trial conducted in Indonesia to investigate the effect of vitamin A supplementation on mortality in children. We find that within‐cluster correlations for these data are very small and that taking into account the clustering does not substantially affect inferences in this case. Additional calculations show that small within‐cluster correlations (though larger than those found in the vitamin A data) may have a large impact on efficacy inferences. We also present the results of a simulation study that demonstrates the validness of the proposed approach for finite sample sizes. Copyright © 2002 John Wiley & Sons, Ltd.