Implementation of Backward Induction for Sequentially Adaptive Clinical Trials
In many clinical trials, patients are enrolled and data are collected sequentially, with interim decisions, including what treatment the next patient should receive and whether or not the trial should be terminated or continued, being based on the accruing data. This naturally leads to application o...
| Publié dans: | Journal of Computational and Graphical Statistics. - American Statistical Association, the Institute of Mathematical Statistics, and the Interface Foundation of North America, 1992. - 15(2006), 2, Seite 398-413 |
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| Auteur principal: | |
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| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2006
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| Accès à la collection: | Journal of Computational and Graphical Statistics |
| Sujets: | Bayesian sequential analysis Decision theory Dynamic programming Behavioral sciences Economics Health sciences Mathematics Philosophy Applied sciences Information science |
| Résumé: | In many clinical trials, patients are enrolled and data are collected sequentially, with interim decisions, including what treatment the next patient should receive and whether or not the trial should be terminated or continued, being based on the accruing data. This naturally leads to application of Bayesian sequential procedures for trial monitoring. This article discusses the implementation and computational tasks involved in the use of backward induction for making decisions during a clinical trial. An efficient method is presented for storing and retrieving decision tables that represent the decision trees characterizing all possible decisions made when implementing a clinical trial using backward induction. We address the general computational needs, and illustrate the ideas with a specific example of a two-arm trial with a binary outcome and a maximum sample size of 200 patients. |
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| ISSN: | 10618600 |