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Stat Med. 2016 Sep 20;35(21):3745-59. doi: 10.1002/sim.6952. Epub 2016 Mar 31.

Power/sample size calculations for assessing correlates of risk in clinical efficacy trials.

Author information

1
Vaccine and Infectious Disease and Public Health Sciences Divisions, Fred Hutchinson Cancer Research Center, Seattle, 98109, Washington, U.S.A.

Abstract

In a randomized controlled clinical trial that assesses treatment efficacy, a common objective is to assess the association of a measured biomarker response endpoint with the primary study endpoint in the active treatment group, using a case-cohort, case-control, or two-phase sampling design. Methods for power and sample size calculations for such biomarker association analyses typically do not account for the level of treatment efficacy, precluding interpretation of the biomarker association results in terms of biomarker effect modification of treatment efficacy, with detriment that the power calculations may tacitly and inadvertently assume that the treatment harms some study participants. We develop power and sample size methods accounting for this issue, and the methods also account for inter-individual variability of the biomarker that is not biologically relevant (e.g., due to technical measurement error). We focus on a binary study endpoint and on a biomarker subject to measurement error that is normally distributed or categorical with two or three levels. We illustrate the methods with preventive HIV vaccine efficacy trials and include an R package implementing the methods.

KEYWORDS:

case-cohort design; case-control design; immune response biomarkers; measurement error; principal stratification; two-phase sampling design; vaccine efficacy trial

PMID:
27037797
PMCID:
PMC4965346
DOI:
10.1002/sim.6952
[Indexed for MEDLINE]
Free PMC Article

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