Keywords: conditional power, combination test, Type I error rate control, fixed and random effects models, non-inferiority
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 non-inferiority 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 due to 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.