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Updated: Jul 25, 2025

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

Michael J Martens1,2, Soyoung Kim1,2, Kwang Woo Ahn1,2

  • 1Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.

Biometrics
|June 26, 2023
PubMed
Summary

Determining sample size and statistical power for biomedical studies is essential. This research introduces a simplified, general method for calculating sample size and power when analyzing multiple variables in regression models, improving study design efficiency.

Keywords:
Cox regressionFine-Gray modelgeneralized linear modelssample size/power determinationstratificationstudy design

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Area of Science:

  • Biostatistics
  • Biomedical Research Methodology

Background:

  • Accurate sample size and power calculations are critical for biomedical study design.
  • Covariate adjustment and analysis of multiple variables (e.g., treatments, risk factors) are common in regression modeling.
  • Existing methods for sample size/power determination with multiple predictors and correlated covariates are often complex or rely on time-consuming simulations.

Purpose of the Study:

  • To propose a simpler, general approach for sample size and power determination.
  • To provide accurate calculations for studies testing multiple parameters in various regression models.
  • To address the complexities arising from multiple variables of interest and covariate correlations.

Main Methods:

  • Development of a general formula for sample size and power calculations.
  • Application to generalized linear models, and ordinary and stratified Cox and Fine-Gray models.
  • Validation through rigorous simulations and theoretical derivations.

Main Results:

  • The proposed formulas accurately determine sample sizes meeting study specifications for type I error rate and statistical power.
  • Demonstrated accuracy across multiple commonly used regression models.
  • The method simplifies complex sample size/power calculations.

Conclusions:

  • The developed approach offers a more accessible and efficient method for sample size and power determination in complex biomedical studies.
  • This facilitates robust study design when assessing multiple variables and covariates.
  • The findings support improved planning and execution of biomedical research requiring regression analysis.