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Comparative Analysis of Group Sequential Designs Tests for Randomized Controlled Clinical Trials: A Model Study on Two-Sided Tests for Comparing Two Treatments
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作者 Mehmet Ali Sungur emine arzu kanik 《Open Journal of Statistics》 2012年第1期60-72,共13页
Clinical trials are usually long term studies and it seems impossible to reach all required subjects at the same time. Performing interim analyses and monitoring results may provide early termination of trial after ob... Clinical trials are usually long term studies and it seems impossible to reach all required subjects at the same time. Performing interim analyses and monitoring results may provide early termination of trial after obtaining significant results. The aim of this study is comparing group sequential tests in respect to advantage of sample size reduction and early termination. In this study, 4 test types used in group sequential designs were compared with fixed sample size design test and each other. Comparisons were done according to two-sided tests for comparing two treatments. In this sense, 1080 models were performed. In models, 2 different Type I errors, 2 different powers, 5 different analysis groups, 6 different effect sizes and 9 different variances selections were considered. All test types increased the maximum sample size in different manner, compared with fixed sample size design. Each test had different critical values to reject H0 hypothesis, at the same type I error rate and number of analyses conditions. Selection of test type used in group sequential designs depends on a few characteristics, as reducing sample size, early termination and detecting minimal effect size. Test performance is highly related with selected Type I error rate, power and number of analyses. In addition to these statistical characteristics, researchers should decide test type with respect to other trial conditions as the issue of trial, reaching subjects easy or not and importance of early termination. 展开更多
关键词 GROUP SEQUENTIAL DESIGNS GROUP SEQUENTIAL Test Types Interim Analysis Monitoring
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