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Using the entire history in the analysis of nested case cohort samples
1Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Kresge 803B, Boston, MA 02115, U.S.A.
Statistics in Medicine
|February 25, 2016
Summary
Countermatching designs with pseudolikelihood estimation offer significant efficiency gains for survival analysis, especially with time-varying covariates. This approach improves upon traditional case-cohort and nested case-control methods.
Area of Science:
- Biostatistics
- Epidemiology
- Survival Analysis
Background:
- Countermatching designs can enhance estimation efficiency over simple matching or case-cohort designs.
- Availability of good surrogate variables is key for countermatching effectiveness.
- Extending pseudolikelihood estimation to Cox models with time-varying covariates is crucial.
Purpose of the Study:
- To extend pseudolikelihood estimation for Cox models under countermatching designs to include time-varying covariates.
- To implement pseudolikelihood with calibrated weights for improved efficiency in nested case-control designs with time-varying variables.
- To evaluate the efficiency of these methods through simulation and real-world data.
Main Methods:
- Extension of pseudolikelihood estimation for Cox models under countermatching designs.
- Implementation of pseudolikelihood with calibrated weights for nested case-control designs.
- Simulation studies considering binary and continuous time-dependent variables, including interactions.
- Application to the Colorado Plateau uranium miners cohort data.
Main Results:
- Pseudolikelihood with calibrated weights under countermatching demonstrated substantial efficiency gains compared to case-cohort designs.
- Calibrated pseudolikelihood estimators were more efficient than standard pseudolikelihood estimators.
- Countermatching designs yielded more efficient estimators than case-cohort designs across simulated scenarios.
- A general method for generating survival times with time-varying covariates was presented.
Conclusions:
- Pseudolikelihood estimation with calibrated weights under countermatching designs is highly efficient for survival analysis with time-varying covariates.
- Countermatching designs offer advantages over case-cohort designs in specific scenarios, particularly with good surrogate variables.
- The developed methods provide valuable tools for epidemiological research involving complex time-dependent exposure data.
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