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Longitudinal studies of binary response data following case-control and stratified case-control sampling: design and
Jonathan S Schildcrout1, Paul J Rathouz
1Departments of Biostatistics and Anesthesiology, Vanderbilt University School of Medicine, 1161 21st Avenue South, S-2323 Medical Center North, Nashville, Tennessee 37232, USA. jonathan.schildcrout@vanderbilt.edu
Biometrics
|August 14, 2009
Summary
This study introduces a semiparametric model for longitudinal studies using case-control sampling. It reveals that while some estimates are robust, others like intercept and time-varying covariates are sensitive to model misspecification.
Area of Science:
- Biostatistics
- Epidemiology
- Longitudinal Data Analysis
Background:
- Case-control sampling is common in epidemiological studies.
- Longitudinal studies track changes over time, crucial for understanding disease progression.
- Integrating these methods presents analytical challenges.
Purpose of the Study:
- To propose a semiparametric modeling framework for longitudinal studies with case-control sampling.
- To assess the impact of population prevalence and model misspecification on parameter estimates.
- To investigate design factors influencing study efficiency.
Main Methods:
- Developed a marginal longitudinal binary response model.
- Incorporated an ancillary model for case-control status.
- Utilized population prevalence to compute offset terms for model fitting.
Main Results:
- Time-invariant covariate estimates (excluding intercept) showed robustness.
- Intercept and time-varying covariate estimates were sensitive to population prevalence and ancillary model misspecification.
- Study efficiency is impacted by sampling variable choice, stratification, and model flexibility.
Conclusions:
- The proposed semiparametric framework offers a method for analyzing longitudinal case-control data.
- Careful consideration of population prevalence and model specification is crucial for accurate inference.
- Optimizing design elements can enhance the efficiency of such studies, as demonstrated with ADHD symptom research.
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