@article{ref1,
title="Variable selection on large case-crossover data: Application to a registry-based study of prescription drugs and road traffic crashes",
journal="Pharmacoepidemiology and drug safety",
year="2014",
author="Avalos, Marta and Orriols, Ludivine and Pouyes, Hélène and Grandvalet, Yves and Thiessard, Frantz and Lagarde, Emmanuel",
volume="23",
number="2",
pages="140-151",
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.<p /> <p>Language: en</p>",
language="en",
issn="1053-8569",
doi="10.1002/pds.3539",
url="http://dx.doi.org/10.1002/pds.3539"
}