Document Detail


Approaches for optimal sequential decision analysis in clinical trials.
MedLine Citation:
PMID:  9750245     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Unlike traditional approaches, Bayesian methods enable formal combination of expert opinion and objective information into interim and final analyses of clinical trial data. However, most previous Bayesian approaches have based the stopping decision on the posterior probability content of one or more regions of the parameter space, thus implicitly determining a loss and decision structure. In this paper, we offer a fully Bayesian approach to this problem, specifying not only the likelihood and prior distributions but appropriate loss functions as well. At each data monitoring point, we enumerate the available decisions and investigate the use of backward induction, implemented via Monte Carlo methods, to choose the optimal course of action. We then present a forward sampling algorithm that substantially eases the analytic and computational burdens associated with backward induction, offering the possibility of fully Bayesian optimal sequential monitoring for previously untenable numbers of interim looks. We show that forward sampling can always identify the optimal sequential strategy in the case of a one-parameter exponential family with a conjugate prior and monotone loss functions as well as the best member of a certain class of strategies when backward induction is infeasible. Finally, we illustrate and compare the forward and backward approaches using data from a recent AIDS clinical trial.
Authors:
B P Carlin; J B Kadane; A E Gelfand
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Biometrics     Volume:  54     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  1998 Sep 
Date Detail:
Created Date:  1998-11-03     Completed Date:  1998-11-03     Revised Date:  2007-11-15    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  964-75     Citation Subset:  IM; X    
Affiliation:
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis 55455, USA. brad@muskie.biostat.umn.edu
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MeSH Terms
Descriptor/Qualifier:
AIDS-Related Opportunistic Infections / prevention & control
Anti-Infective Agents / pharmacology
Bayes Theorem
Biometry / methods*
Clinical Trials as Topic / statistics & numerical data*
Decision Support Techniques*
Double-Blind Method
Humans
Models, Statistical
Pyrimethamine / pharmacology
Randomized Controlled Trials as Topic / methods
Toxoplasmosis, Cerebral / prevention & control
Grant Support
ID/Acronym/Agency:
1-R01-AI41966/AI/NIAID NIH HHS; N01-AI05073/AI/NIAID NIH HHS
Chemical
Reg. No./Substance:
0/Anti-Infective Agents; 58-14-0/Pyrimethamine

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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