University of Washington - Department of Biostatistics
In this talk we discuss the application of Bayesian methods in the design of clinical trials.
In the first part of the talk we discuss sample size determination. A broad range of frequentist and Bayesian methods for sample size determination can be described as choosing the smallest sample that is sufficient to achieve some set of goals. An example for the frequentist is seeking the smallest sample size that is sufficient to achieve a desired power at a specified significance level. An example for the Bayesian is seeking the smallest sample size necessary to obtain, in expectation, a fixed width posterior probability interval. We explore parallelisms between Bayesian and frequentist methods for determining sample size. We provide a simple but general and pragmatic framework for investigating the relationship between the two approaches, based on identifying mappings to connect the Bayesian and frequentist inputs necessary to obtain the same sample size. We illustrate this mapping with examples, highlighting a somewhat surprising ``approximate functional correspondence'' between power-based and information-based optimal sample sizes.
In the second part of the talk we consider clinical studies with health outcomes occurring over a long period of time with data collected using follow-up interviews with patients. When these interviews are widely spaced, scheduling can affect the efficiency of the study design and the issue arises of what is the optimal schedule. We analyze this problem in studies of times to event from a Bayesian decision-theoretic viewpoint. We discuss computational approaches for the sequential choice of follow-up times using dynamic programming and illustrate it in the setting of a two-stage design. Our results demonstrate that important gains in efficiency can be achieved using a Bayesian sequential timing of follow-up.