Not Too Big, Not Too Small: A Goldilocks Approach To Sample Size Selection

    Authors

    K. R. Broglio; J. T. Connor;S. M. Berry

    Comments

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    Abbreviated Journal Title

    J. Biopharm. Stat.

    Keywords

    Sample size; Predictive probabilities; Sequential design; Bayesian; adaptive trial design; MONITORING CLINICAL-TRIALS; STAGE BREAST-CANCER; PREDICTIVE PROBABILITY; WOMEN; Pharmacology & Pharmacy; Statistics & Probability

    Abstract

    We present a Bayesian adaptive design for a confirmatory trial to select a trial's sample size based on accumulating data. During accrual, frequent sample size selection analyses are made and predictive probabilities are used to determine whether the current sample size is sufficient or whether continuing accrual would be futile. The algorithm explicitly accounts for complete follow-up of all patients before the primary analysis is conducted. We refer to this as a Goldilocks trial design, as it is constantly asking the question, "Is the sample size too big, too small, or just right?" We describe the adaptive sample size algorithm, describe how the design parameters should be chosen, and show examples for dichotomous and time-to-event endpoints.

    Journal Title

    Journal of Biopharmaceutical Statistics

    Volume

    24

    Issue/Number

    3

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    685

    Last Page

    705

    WOS Identifier

    WOS:000334334600014

    ISSN

    1054-3406

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