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Stat Med. 2018 Dec 20;37(29):4374-4385. doi: 10.1002/sim.7927. Epub 2018 Aug 8.

Reliably picking the best endpoint.

Author information

1
Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD.

Abstract

Endpoint selection in clinical trials involves a variety of considerations. One important consideration is the sample size required to power a future clinical trial. In this work, we define the sample size ratio, θ, as the ratio of sample sizes required to power a future trial. We consider in detail the setting of continuous endpoints where a Welch's t-statistic is used to analyze the data. We develop an estimator that depends on the squared ratio of estimated standardized treatment effects, and the quadrant on the plane in which they fall. We evaluate bootstrap and profile likelihood methods for construction of confidence intervals. Generalizations to other endpoints and testing of nonnested models are discussed. The methods are applied to analyze two different assays that measure antibody abundance using data from an Ebola vaccine field trial.

KEYWORDS:

bootstrap; clinical trials; primary endpoint; profile likelihood; ratio of normals

PMID:
30091264
DOI:
10.1002/sim.7927

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