The hidden factor: accounting for covariate effects in power and sample size computation for a binary trait.

ABSTRACT:

Motivation

Accurate power and sample size estimation is crucial to the design and analysis of genetic association studies. When analyzing a binary trait via logistic regression, important covariates such as age and sex are typically included in the model. However, their effects are rarely properly considered in power or sample size computation during study planning. Unlike when analyzing a continuous trait, the power of association testing between a binary trait and a genetic variant depends, explicitly, on covariate effects, even under the assumption of gene-environment independence. Earlier work recognizes this hidden factor but the implemented methods are not flexible. We thus propose and implement a generalized method for estimating power and sample size for (discovery or repli

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC10070038 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature