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Published on: October 23, 2020
A modified approach to estimating sample size for simple logistic regression with one continuous covariate.
I Novikov1, N Fund, L S Freedman
1Biostatistics Unit, Gertner Institute for Epidemiology and Health Policy Research, Israel. ilian@gertner.health.gov.il
Statistics in Medicine
|October 14, 2009
Summary
Calculating sample size for logistic regression (LR) can be complex. This study proposes a modified method for accurate sample size estimation with continuous covariates, improving upon existing approaches.
Area of Science:
- Biostatistics
- Statistical Modeling
Background:
- Sample size calculation for logistic regression (LR) with continuous covariates lacks standardized methods.
- Existing methods can yield significantly different results and may require non-intuitive parameters like mean covariate prevalence.
Purpose of the Study:
- To evaluate the accuracy of commonly used sample size calculation methods for simple logistic regression.
- To propose an improved method for sample size estimation that uses population prevalence.
Main Methods:
- Comparative analysis of two common sample size calculation methods via simulations.
- Development of a modified Hsieh et al. method incorporating Schouten's sample size formula for t-tests.
Main Results:
- Simulations revealed substantial power discrepancies from nominal values for one method, particularly with large covariate effects.
- Another method showed poor performance when population prevalence was used instead of the required mean covariate prevalence.
Conclusions:
- Existing sample size methods for logistic regression with continuous covariates are unreliable.
- The proposed modified method enhances accuracy by utilizing population prevalence and a robust t-test formula.
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