ABSTRACT
The collection of safety and tolerability data in all clinical trials goes well beyond the data collected to address specific safety hypotheses, which may be developed from the chemical or biological properties of the product, or possibly from observations from early-phase nonclinical and clinical trials. Adding to the complexity, the set of possible adverse effects is very large and new unanticipated effects are always possible. Moreover, confirmatory clinical trials to test the efficacy hypotheses usually have large sample sizes, and this may result in many more adverse event types, most of which were not expected based on the pharmacological profile of the product, preclinical experiments in animals, or in vitro studies. Hence there is potential for drawing false positive conclusions and the need for understanding the multiplicity aspects in safety signal detection. Safety assessment continues into the post-marketing phase initially with clinical trials designed specifically to address possible safety issues, and later with pharmacovigilance based on large databases of patient electronic health records and systems that collect spontaneous reports of adverse events. While the multiplicity considerations differ during different phases of drug development, they are always an important component in the analysis and interpretation of clinical safety data. In their discussion of safety analysis in the pre-marketing phases, Chuang-Stein and Xia (2013) identify multiplicity as a key issue that needs to be included in the planning. In this chapter we consider multiplicity in the planning and interpretation of safety assessment throughout the drug and vaccine development process. We describe both frequentist error-controlling methods and Bayes (in particular, empirical Bayes) methods that have been developed to address multiplicity in the evaluation of clinical safety data. In Chapter 7 we continue the discussion of multiplicity in the data mining post-marketing environment for safety signal detection and pharmacovigilance, which have been noted by Gould (2007) and others.
