2021 年 42 巻 1 号 p. 15-32
After the implementations of the revised “Ministerial Ordinance on Good Post-marketing Study Practice for Drugs” in April 2018, the Pharmaceuticals and Medical Devices Agency requires to conduct more efficient and effective post-marketing study (PMS). To design a PMS, it is important to define a research question based on the information of clinical trials, the characteristics of target diseases and the product profile. Nevertheless, designing a PMS faces some key challenges: (1) it is difficult to specify a research question due to strong exploratory aspects; (2) the results of PMS are not so useful for practical pharmacovigilance activities despite spending a huge resource/cost and (3) the accumulated experience and knowledge from existing clinical trials are not utilized. It is potentially difficult to address those challenges by the frequentist approach, although it can be used in PMS. We propose a Bayesian approach to address these three key challenges for a single arm PMS. Under the Bayesian approach, we can set and answer a research question in a probabilistic way and then offer some added values to frequentist method. Moreover, we discuss the sample size calculation for the research question based on the Bayesian approach and practical issues of implementing the Bayesian approach.