Title

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

Authors

Authors

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

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

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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