TY  - JOUR
PY  - 2014//
TI  - Variable selection on large case-crossover data: Application to a registry-based study of prescription drugs and road traffic crashes
JO  - Pharmacoepidemiology and drug safety
A1  - Avalos, Marta
A1  - Orriols, Ludivine
A1  - Pouyes, Hélène
A1  - Grandvalet, Yves
A1  - Thiessard, Frantz
A1  - Lagarde, Emmanuel
SP  - 140
EP  - 151
VL  - 23
IS  - 2
N2  - 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>
LA  - en
SN  - 1053-8569
UR  - http://dx.doi.org/10.1002/pds.3539
ID  - ref1
ER  -