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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Analysis of multiple exposures in the case-crossover design via sparse conditional likelihood.
Marta Avalos1, Yves Grandvalet, Nuria Duran Adroher
1ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, Univ. Bordeaux, F-33000, Bordeaux, France. marta.avalos@isped.u-bordeaux2.fr
Statistics in Medicine
|March 16, 2012
Summary
Sparse methods like lasso enhance conditional logistic regression for case-crossover studies. These techniques offer valuable variable selection, especially with many predictors, aiding analysis of drug exposure and crash risk.
Area of Science:
- Biostatistics
- Epidemiology
- Statistical Computing
Background:
- Case-crossover studies are frequently used in pharmacoepidemiology.
- High-dimensional data in these studies pose challenges for traditional regression models.
- Variable selection is crucial for identifying risk factors accurately.
Purpose of the Study:
- To adapt and implement sparse regression methods, including the least absolute shrinkage and selection operator (lasso), elastic net, and bootstrapped lasso, for conditional logistic regression.
- To evaluate the performance of these sparse methods against conventional strategies in case-crossover studies.
- To analyze the association between medication exposure and injurious road traffic crashes in elderly drivers.
Main Methods:
- Adaptation of lasso, elastic net, and bootstrapped lasso for conditional logistic regression.
- Development of an R implementation for these sparse methods.
- Simulation studies to assess performance of sparse and conventional methods.
- Empirical application to a real-world dataset on drug exposure and traffic crash risk.
Main Results:
- Sparse methods, particularly certain variants, demonstrate value in case-crossover studies with numerous variables.
- Performance comparison through simulations indicates the utility of these advanced techniques.
- Empirical analysis successfully applied sparse methods to investigate drug-related crash risk in elderly drivers.
Conclusions:
- Sparse regression methods are valuable tools for case-crossover studies, especially when dealing with a large number of potential predictors.
- While controlling the false discovery rate remains a challenge, it is also an issue with conventional methods.
- These adapted sparse methods offer a global analysis of dependencies, improving risk factor identification.
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