Title

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

Keywords

Bayesian adaptive trial design; Predictive probabilities; Sample size; Sequential design.

Abstract

We present a Bayesian adaptive design for a confirmatory trial to select a trials 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. © 2014 Taylor and Francis Group, LLC.

Publication Date

5-4-2014

Publication Title

Journal of Biopharmaceutical Statistics

Volume

24

Issue

3

Number of Pages

685-705

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/10543406.2014.888569

Socpus ID

84899013925 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84899013925

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