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Adaptive and repeated cumulative meta‐analyses of safety data during a new drug development process
Author(s) -
Quan Hui,
Ma Yingqiu,
Zheng Yan,
Cho Meehyung,
Lorenzato Christelle,
Hecquet Carole
Publication year - 2015
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1669
Subject(s) - frequentist inference , sample size determination , computer science , meta analysis , type i and type ii errors , bayesian probability , process (computing) , econometrics , statistics , medicine , bayesian inference , mathematics , artificial intelligence , operating system
During a new drug development process, it is desirable to timely detect potential safety signals. For this purpose, repeated meta‐analyses may be performed sequentially on accumulating safety data. Moreover, if the amount of safety data from the originally planned program is not enough to ensure adequate power to test a specific hypothesis (e.g., the noninferiority hypothesis of an event of interest), the total sample size may be increased by adding new studies to the program. Without appropriate adjustment, it is well known that the type I error rate will be inflated because of repeated analyses and sample size adjustment. In this paper, we discuss potential issues associated with adaptive and repeated cumulative meta‐analyses of safety data conducted during a drug development process. We consider both frequentist and Bayesian approaches. A new drug development example is used to demonstrate the application of the methods. Copyright © 2015 John Wiley & Sons, Ltd.