The utility of Bayesian predictive probabilities for interim monitoring of clinical trials

    Authors

    B. R. Saville; J. T. Connor; G. D. Ayers;J. Alvarez

    Comments

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    Abbreviated Journal Title

    Clin. Trials

    Keywords

    SEQUENTIAL TEST DESIGNS; 2-STAGE DESIGNS; CANCER; POWER; SIZE; Medicine, Research & Experimental

    Abstract

    Background Bayesian predictive probabilities can be used for interim monitoring of clinical trials to estimate the probability of observing a statistically significant treatment effect if the trial were to continue to its predefined maximum sample size. Purpose We explore settings in which Bayesian predictive probabilities are advantageous for interim monitoring compared to Bayesian posterior probabilities, p-values, conditional power, or group sequential methods. Results For interim analyses that address prediction hypotheses, such as futility monitoring and efficacy monitoring with lagged outcomes, only predictive probabilities properly account for the amount of data remaining to be observed in a clinical trial and have the flexibility to incorporate additional information via auxiliary variables. Limitations Computational burdens limit the feasibility of predictive probabilities in many clinical trial settings. The specification of prior distributions brings additional challenges for regulatory approval. Conclusions The use of Bayesian predictive probabilities enables the choice of logical interim stopping rules that closely align with the clinical decision-making process.

    Journal Title

    Clinical Trials

    Volume

    11

    Issue/Number

    4

    Publication Date

    1-1-2014

    Document Type

    Article; Proceedings Paper

    Language

    English

    First Page

    485

    Last Page

    493

    WOS Identifier

    WOS:000340305800013

    ISSN

    1740-7745

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