Bayesian approaches to problems in drug safety and adaptive clinical trial designs.

The efficacy, safety, and cost of pharmaceutical products are critical issues in society today. Motivated both financially and ethically by these concerns, the pharmaceutical industry has continually worked to develop methods which provide more efficient and ethical assessments of the safety and efficacy of pharmaceutical products. There is an increased emphasis on more targeted treatments with a focus on better patient outcomes. In this vein, recent applications of advanced statistical methods have allowed companies to reduce the costs of getting safe and effective products to market—savings that can be passed on to consumers in the form of price cuts or additional investment in research and development. Among the methods that have become increasingly important in drug development are adaptive experimental designs. We first investigate the impacts of misclassification of response on a Bayesian adaptive design. A primary argument for the use of adaptive designs is the efficiency one gains over implementing a traditional fixed design. We examine the design’s performance under misclassified responses and compare it to the situation for which we account for the misclassification in a Bayesian model. Next, we examine the utility of safety lab measures collected during the clinical development of a drug. These labs are used to characterize a drug’s safety profile and their scope can be limited when reasonably confident of no associated safety concern, facilitating reduced costs and less subject burden. We consider the use of a Bayesian generalized linear model and investigate the use of conditional means priors and power priors for the regression coefficients used in the analysis of safety lab measures. Finally, we address the need for transparent benefit-risk assessment methods that combine safety and efficacy data and allow straight forward comparisons of treatment options. We begin by developing interval estimates on a commonly-used benefit-risk ratio. We then propose the use of a Bayesian generalized linear model to jointly assess safety and efficacy, allowing for direct comparisons of competing treatment options utilizing posterior 95% credible sets and predictive probabilities.