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Power and sample size for multivariate logistic modeling of unmatched case-control studies
Mitchell H Gail1, Sebastien Haneuse2
11 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Statistical Methods in Medical Research
|November 18, 2017
Summary
Calculating sample sizes for complex case-control studies is challenging. This study provides methods and software for sample size calculations in multivariate logistic regression, aiding study design and feasibility assessment.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Sample size calculations are crucial for designing and evaluating the feasibility of case-control studies.
- Existing methods are limited for multivariate unconditional logistic analysis.
- There is a need for accessible tools for complex case-control study designs.
Purpose of the Study:
- To present the theory for sample size calculations in multivariate logistic regression for case-control studies.
- To enable detection of scalar exposure effects or interactions while controlling for covariates.
- To provide practical tools for researchers conducting complex epidemiological studies.
Main Methods:
- Outlined the theoretical framework for sample size calculations.
- Presented both analytical and simulation-based methods.
- Developed and linked to corresponding software for practical application.
Main Results:
- Provided a theoretical basis for sample size determination in multivariate logistic regression.
- Demonstrated the utility of both analytical and simulation approaches.
- Made software available to facilitate these calculations.
Conclusions:
- This work addresses the gap in sample size calculation methods for complex case-control studies.
- The provided theory and software support the design of studies using multivariate logistic regression.
- Researchers can now more effectively plan and assess the feasibility of intricate case-control studies.
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