Regulatory science comprises the tools, standards, and approaches that regulators use to assess safety, efficacy, quality, and performance of drugs and medical devices. A major focus of regulatory science is the design and analysis of clinical trials. These clinical experiments help us learn about what works clinically and what does not work. The results of clinical trials support therapeutic and policy decisions. Decision making also arises when designing clinical trials. Investigators make many decisions regarding various aspects of how they will carry out the study, such as the primary objective of the study, primary and secondary endpoints, methods of analysis, sample size, etc. Many scientists have advocated greater application of Bayesian statistical inference in regulatory science, and applications of Bayesian methods in drug and device development continue to increase. This talk presents a vision of the drug and device development framework and the way Bayesian inference fits naturally within it. In particular, I advocate greater application of Bayesian decision theory in clinical evaluation of therapeutics. I present some examples that illustrate how one can use decision theory in the design of a clinical study. I also point out some of the challenges one encounters when applying decision theory to clinical research.