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Variable selection on large case-crossover data: application to a registry-based study of prescription drugs and road traffic crashes.
Pharmacoepidemiol Drug Saf. 2014 Feb;23(2):140-51
Authors: Avalos M, Orriols L, Pouyes H, Grandvalet Y, Thiessard F, Lagarde E, CESIR research group
Abstract
PURPOSE: In exploratory analyses of pharmacoepidemiological data from large populations with large number of exposures, both a conceptual and computational problem is how to screen hypotheses using probabilistic reasoning, selecting drug classes or individual drugs that most warrant further hypothesis testing.
METHODS: We report the use of a shrinkage technique, the Lasso, in the exploratory analysis of the data on prescription drugs and road traffic crashes, resulting from the case-crossover matched-pair interval approach described by Orriols and colleagues (PLoS Med 2010; 7:e1000366). To prevent false-positive results, we consider a bootstrap-enhanced version of the Lasso. To highlight the most stable results, we extensively examine sensitivity to the choice of referent window.
RESULTS: Antiepileptics, benzodiazepine hypnotics, anxiolytics, antidepressants, antithrombotic agents, mineral supplements, drugs used in diabetes, antiparkinsonian treatment, and several cardiovascular drugs showed suspected associations with road traffic accident involvement or accident responsibility.
CONCLUSION: These results, in relation to other findings in the literature, provide new insight and may generate new hypotheses on the association between prescription drugs use and impaired driving ability. Copyright © 2013 John Wiley & Sons, Ltd.
PMID: 24136855 [PubMed - in process]