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Abstract

In this paper we describe a decision analysis that was designed to help a pharmaceutical company decide whether to continue development of a drug after the completion of a Phase II program. We use a Bayesian hierarchical model to integrate data from three Phase II studies, data from related studies reported in the literature, and prior assumptions about the pattern of response across dose and dosing frequency. The product of this model is a posterior distribution of the efficacy and tolerance parameters. This distribution is then combined with marketing data in a decision model. The decision model includes a second (post-Phase III) decision point. The main goal of this report is to present the methodology of this decision analysis. There is also some limited presentation of results.

Keywords

Decision analysis Hierarchical models Bayesian methods Pharmaceutical Drug development

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A Decision Analysis for an end of Phase II Go/Stop Decision

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