| Approaches for optimal sequential decision analysis in clinical trials. | |
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MedLine Citation:
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PMID: 9750245 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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. |
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Authors:
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B P Carlin; J B Kadane; A E Gelfand |
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Publication Detail:
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Type: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S. |
Journal Detail:
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Title: Biometrics Volume: 54 ISSN: 0006-341X ISO Abbreviation: Biometrics Publication Date: 1998 Sep |
Date Detail:
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Created Date: 1998-11-03 Completed Date: 1998-11-03 Revised Date: 2007-11-15 |
Medline Journal Info:
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Nlm Unique ID: 0370625 Medline TA: Biometrics Country: UNITED STATES |
Other Details:
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Languages: eng Pagination: 964-75 Citation Subset: IM; X |
Affiliation:
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Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis 55455, USA. brad@muskie.biostat.umn.edu |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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AIDS-Related Opportunistic Infections
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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:
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1-R01-AI41966/AI/NIAID NIH HHS; N01-AI05073/AI/NIAID NIH HHS |
| Chemical | |
Reg. No./Substance:
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0/Anti-Infective Agents; 58-14-0/Pyrimethamine |
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