On Stratified Adjusted Tests by Binomial Trials

Shimokawa Asanao () and Miyaoka Etsuo
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Shimokawa Asanao: Department of Mathematics, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo162-8601, Japan
Miyaoka Etsuo: Department of Mathematics, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku , Tokyo162-8601, Japan

The International Journal of Biostatistics, 2017, vol. 13, issue 1, 15

Abstract: To estimate or test the treatment effect in randomized clinical trials, it is important to adjust for the potential influence of covariates that are likely to affect the association between the treatment or control group and the response. If these covariates are known at the start of the trial, random assignment of the treatment within each stratum would be considered. On the other hand, if these covariates are not clear at the start of the trial, or if it is difficult to allocate the treatment within each stratum, completely randomized assignment of the treatment would be performed. In both sampling structures, the use of a stratified adjusted test is a useful way to evaluate the significance of the overall treatment effect by reducing the variance and/or bias of the result. If the trial has a binary endpoint, the Cochran and Mantel-Haenszel tests are generally used. These tests are constructed based on the assumption that the number of patients within a stratum is fixed. However, in practice, the stratum sizes are not fixed at the start of the trial in many situations, and are instead allowed to vary. Therefore, there is a risk that using these tests under such situations would result in an error in the estimated variation of the test statistics. To handle the problem, we propose new test statistics under both sampling structures based on multinomial distributions. Our proposed approach is based on the Cochran test, and the difference between the two tests tends to have similar values in the case of a large number of patients. When the total number of patients is small, our approach yields a more conservative result. Through simulation studies, we show that the new approach could correctly maintain the type I error better than the traditional approach.

Keywords: binary data; random stratum sizes; risk difference; type I error (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/ijb-2016-0047

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