Literature DB >> 3956212

Monitoring clinical trials: conditional or predictive power?

D J Spiegelhalter, L S Freedman, P R Blackburn.   

Abstract

At an interim point in a clinical trial, trial organisers may wish to use the data on the initial series of patients to judge the likely consequences of further patient accrual. Halperin and colleagues (Controlled Clin Trials 3:311-323, 1982) have suggested calculating the power of a continued trial, conditional on the data observed so far and the null and alternative hypothesis specified at the start of the trial. Here we argue that this idea should be extended to obtain the predictive power of the trial, derived by averaging the conditional power with respect to the current belief about the unknown parameters. Although numerical methods are generally required for evaluating the necessary integrals, the results may be presented graphically and enable the statistician to answer the question: "With the data so far, what is the chance that the trial will end up showing a conclusive result?"

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Year:  1986        PMID: 3956212     DOI: 10.1016/0197-2456(86)90003-6

Source DB:  PubMed          Journal:  Control Clin Trials        ISSN: 0197-2456


  25 in total

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7.  Bayesian approaches for comparative effectiveness research.

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Journal:  Clin Trials       Date:  2011-08-30       Impact factor: 2.486

8.  Between-arm comparisons in randomized Phase II trials.

Authors:  Sin-Ho Jung; Stephen L George
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10.  The utility of Bayesian predictive probabilities for interim monitoring of clinical trials.

Authors:  Benjamin R Saville; Jason T Connor; Gregory D Ayers; JoAnn Alvarez
Journal:  Clin Trials       Date:  2014-05-28       Impact factor: 2.486

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