Sample size and power determination for multiparameter evaluation in nonlinear regression models with potential stratification

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....

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Veröffentlicht in:Biometrics 2023-12, Vol.79 (4), p.3916-3928
Hauptverfasser: Martens, Michael J., Kim, Soyoung, Ahn, Kwang Woo
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container_title Biometrics
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creator Martens, Michael J.
Kim, Soyoung
Ahn, Kwang Woo
description 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.
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source MEDLINE; Access via Wiley Online Library; Oxford University Press Journals All Titles (1996-Current)
subjects Computer Simulation
Cox regression
Fine–Gray model
Generalized linear models
Linear Models
Models, Statistical
Parameters
Regression analysis
Regression models
Research Design
Risk Factors
Sample Size
sample size/power determination
Statistical analysis
Statistical models
stratification
study design
title Sample size and power determination for multiparameter evaluation in nonlinear regression models with potential stratification
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