On Two-stage Seamless Adaptive Design in Clinical Trials.
In recent years, the use of adaptive design methods in clinical research and development based on accrued data has become very popular because of its efficiency and flexibility in modifying trial and/or statistical procedures of ongoing clinical trials. One of the most commonly considered adaptive designs is probably a two-stage seamless adaptive trial design that combines two separate studies into one single study. In many cases, study endpoints considered in a two-stage seamless adaptive design may be similar but different (e.g. a biomarker versus a regular clinical endpoint or the same study endpoint with different treatment durations). In this case, it is important to determine how the data collected from both stages should be combined for the final analysis. It is also of interest to know how the sample size calculation/allocation should be done for achieving the study objectives originally set for the two stages (separate studies). In this article, formulas for sample size calculation/allocation are derived for cases in which the study endpoints are continuous, discrete (e.g. binary responses), and contain time-to-event data assuming that there is a well-established relationship between the study endpoints at different stages, and that the study objectives at different stages are the same. In cases in which the study objectives at different stages are different (e.g. dose finding at the first stage and efficacy confirmation at the second stage) and when there is a shift in patient population caused by protocol amendments, the derived test statistics and formulas for sample size calculation and allocation are necessarily modified for controlling the overall type I error at the prespecified level.
Chow
Shein-Chung
SC
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA. sheinchung.chow@duke.edu
Tu
Yi-Hsuan
YH
D035843:Biomedical Research; D002986:Clinical Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D023381:Endpoint Determination; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D018401:Sample Size