title: Robust Bayesian approaches for clinical trial monitoring.
abstract: The interim monitoring and final analysis of data arising from a clinical trial require an inferential method capable of convincing a broad group of potential consumers: doctors; patients; politicians; members of the media, and so on. While Bayesian methods offer a powerful and flexible analytic framework in this setting, this need to convince a diverse community necessitates a practical approach for studying and communicating the robustness of conclusions to the prior specification. In this paper we attempt to characterize the class of priors leading to a given decision (such as stopping the trial and rejecting the null hypothesis) conditional on the observed data. We evaluate the practicality and effectiveness of this procedure over a range of smoothness conditions on the prior class. First, we consider a non-parametric class of priors restricted only in that its elements must have certain prespecified quantiles. We then obtain more precise results by further restricting the prior class, first to a non-parametric class whose members are quasi-unimodal, then to a semi-parametric normal mixture class, and finally to the fully parametric normal family. We illustrate all of our comparisons with a dataset from an AIDS clinical trial that compared the effectiveness of the drug pyrimethamine and a placebo in preventing toxoplasmic encephalitis.
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis 55455-0392, USA.
authors.forename: B P
authors.initials: BP
authors.lastname: Carlin
authors.forename: D J
authors.initials: DJ
authors.lastname: Sargent
mesh_terms: D017088:AIDS-Related Opportunistic Infections / Q000517:prevention & control; D000890:Anti-Infective Agents / Q000627:therapeutic use; D001499:Bayes Theorem; D018849:Controlled Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D004660:Encephalitis / Q000150:complications / Q000517:prevention & control; D006801:Humans; D010919:Placebos; D011739:Pyrimethamine / Q000627:therapeutic use; D014123:Toxoplasmosis / Q000150:complications / Q000517:prevention & control
chemical_list: D000890:Anti-Infective Agents; D010919:Placebos; D011739:Pyrimethamine