A Sample-Size Optimal Bayesian Procedure for Sequential Pharmaceutical Trials

Abstract

Consider a pharmaceutical trial where the consequences of different decisions are expressed on a financial sale. The efficacy of the new drug under consideration has a prior distribution obtained from the underlying biological process, animal experiments, clinical experience, and so forth. In an important paper, Berry and Ho (1988) show how these components are used to establish an optimal (Bayes) sequential procedure, assuming a known constant size at each decision point. We show in this article how it is also possible to optimize with respect to the sample-size rule. This last component of the design, which is missing from most sequential procedures, has the potential to yield considerably larger expected net gains.

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Document Details

Document Type
Technical Report
Publication Date
Mar 05, 1992
Accession Number
ADA248512

Entities

People

  • Jonathan Biele
  • Noel Cressie

Organizations

  • Stanford University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Bayesian Networks
  • Biological Processes
  • Clinical Trials
  • Costs
  • Data Science
  • Decision Theory
  • Information Science
  • Military Research
  • New York
  • Probability
  • Sampling
  • Sequential Analysis
  • Specifications
  • Statistical Decision Theory
  • Statistics
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Clinical Trial Research.
  • Statistical inference.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference