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Evaluation of Cox's model and logistic regression for matched case-control data with time-dependent covariates: a
Karen Leffondré1, Michal Abrahamowicz, Jack Siemiatycki
1Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada.
Statistics in Medicine
|December 16, 2003
Summary
Conventional logistic regression and Cox models with time-dependent covariates show limitations in case-control studies with time-varying exposures. Simulation reveals biases in both methods, highlighting the need for improved risk set manipulation in Cox models.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Case-control studies commonly use logistic regression, which struggles with time-varying covariates.
- Time-varying exposures are prevalent and require appropriate analytical methods.
Purpose of the Study:
- To evaluate the accuracy of Cox models with time-dependent covariates and logistic regression for case-control data with time-varying exposures.
- To investigate the impact of risk set definition and exposure characteristics on Cox model estimates.
Main Methods:
- A simulation study generated hypothetical population data and simulated matched case-control studies.
- Evaluated conventional logistic regression and two versions of the Cox model with time-dependent covariates (current exposure indicator, exposure duration).
Main Results:
- No single model consistently performed well across all scenarios.
- Logistic regression showed discrepancies between odds ratios and hazard ratios, particularly with inter-correlated time-dependent covariates.
- Cox models exhibited under-estimation or over-estimation bias, with the latter proportional to the true effect size.
Conclusions:
- Logistic regression has limitations in handling time-varying exposures and correlated covariates in case-control studies.
- Cox models require careful risk set manipulation to mitigate bias in estimating time-dependent effects.
- Further refinement of risk set definitions in Cox models may reduce estimation bias.
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