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[Unconditioned logistic regression and sample size: a bibliographic review]
Manuel Ortega Calvo1, Aurelio Cayuela Domínguez
1Centro de Salud Pilas, Unidad de Apoyo a la Investigación, Hospitales Universitarios Virgen del Rocío, Sevilla.
Revista Espanola De Salud Publica
|May 25, 2002
Summary
This review explores sample size calculations for logistic regression in epidemiology. It examines various methods and concepts, including predictive constriction and the event per variable rule, offering critical insights for risk prediction.
Area of Science:
- Epidemiology
- Biostatistics
Context:
- Logistic regression is a key tool for risk prediction in epidemiology.
- Calculating appropriate sample sizes is crucial for reliable logistic regression models.
Purpose:
- To review existing solutions for the interface between sample size calculation and logistic regression.
- To critically examine concepts like predictive constriction, ordinal outcomes, and event per variable rules.
Summary:
- The article discusses sample size considerations for logistic regression, covering customized regression, predictive constriction, and ordinal outcomes.
- It reviews the event per variable concept, indicator variables, and the Freeman equation, incorporating skeptical viewpoints.
Impact:
- Provides a comprehensive overview of sample size methodologies for logistic regression.
- Aids researchers in designing robust epidemiological studies and interpreting risk prediction models accurately.