Sample size and power determination for multiparameter evaluation in nonlinear regression models with potential stratification. Biometrics 2023 Dec;79(4):3916-3928
Date
06/26/2023Pubmed ID
37357412Pubmed Central ID
PMC10749377DOI
10.1111/biom.13897Scopus ID
2-s2.0-85162954172 (requires institutional sign-in at Scopus site)Abstract
Sample size and power determination are crucial design considerations for biomedical studies intending to formally test the effects of key variables on an outcome. Other known prognostic factors may exist, necessitating the use of techniques for covariate adjustment when conducting this evaluation. Moreover, the main interest often includes assessing the impact of more than one variable on an outcome, such as multiple treatments or risk factors. Regression models are frequently employed for these purposes, formalizing this assessment as a test of multiple regression parameters. But, the presence of multiple variables of primary interest and correlation between covariates can complicate sample size/power calculation. Given the paucity of available sample size formulas for this context, these calculations are often performed via simulation, which can be both time-consuming as well as demanding extensive probability modeling. We propose a simpler, general approach to sample size and power determination that may be applied when testing multiple parameters in commonly used regression models, including generalized linear models as well as ordinary and stratified versions of the Cox and Fine-Gray models. Through both rigorous simulations and theoretical derivations, we demonstrate the formulas' accuracy in producing sample sizes that will meet the type I error rate and power specifications of the study design.
Author List
Martens MJ, Kim S, Ahn KWAuthors
Kwang Woo Ahn PhD Director, Professor in the Data Science Institute department at Medical College of WisconsinSoyoung Kim PhD Associate Professor in the Data Science Institute department at Medical College of Wisconsin
Michael Martens PhD Assistant Professor in the Data Science Institute department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
Computer SimulationLinear Models
Models, Statistical
Research Design
Risk Factors
Sample Size
