Interim analysis of binary outcome data in clinical trials: a comparison of five estimators.
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In clinical trials, where the outcome of interest is the occurrence of an event over a fixed time period, estimation of the event proportion at interim analysis can form a basis for decision-making such as early trial termination, sample size re-estimation, and/or dropping inferior treatment arms. In addition to derivation of mean squared error under an exponential time-to-event distribution, we performed a simulation study to examine the performance of five estimators of the event proportion when time to the event is assessable. The simulation results showed advantages of the Kaplan-Meier estimator over others in terms of robustness, and the bias and variability of the event proportion estimate. An example was given to illustrate how the estimators affect dropping treatment arms in a multi-arm multi-stage adaptive trial. We recommended the use of the Kaplan-Meier estimator and discourage the use of other estimators that discard the inherent time-to-event information.
Published Version (Please cite this version)10.1080/10543406.2018.1559852
Publication InfoChow, Shein-Chung; Lu, Qing Shu; & Tse, Siu-Keung (2019). Interim analysis of binary outcome data in clinical trials: a comparison of five estimators. Journal of biopharmaceutical statistics, 29(2). pp. 400-410. 10.1080/10543406.2018.1559852. Retrieved from https://hdl.handle.net/10161/18194.
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Professor of Biostatistics and Bioinformatics
My research interest includes statistical methodology development and application in the area of biopharmaceutical/clinical statistics such as bioavailability and bioequivalence, clinical trials, bridging studies, medical devices, and translational research/medicine. Most recently, I am interested in statistical methodology development for the use of adaptive design methods in clinical trials and methodology development for assessment of biosimilarity of follow-on biologics. In addition, I