title: Prescription-drug-related risk in driving: comparing conventional and lasso shrinkage logistic regressions.
abstract: BACKGROUND
  Large data sets with many variables provide particular challenges when constructing analytic models. Lasso-related methods provide a useful tool, although one that remains unfamiliar to most epidemiologists.
  
  
  METHODS
  We illustrate the application of lasso methods in an analysis of the impact of prescribed drugs on the risk of a road traffic crash, using a large French nationwide database (PLoS Med 2010;7:e1000366). In the original case-control study, the authors analyzed each exposure separately. We use the lasso method, which can simultaneously perform estimation and variable selection in a single model. We compare point estimates and confidence intervals using (1) a separate logistic regression model for each drug with a Bonferroni correction and (2) lasso shrinkage logistic regression analysis.
  
  
  RESULTS
  Shrinkage regression had little effect on (bias corrected) point estimates, but led to less conservative results, noticeably for drugs with moderate levels of exposure. Carbamates, carboxamide derivative and fatty acid derivative antiepileptics, drugs used in opioid dependence, and mineral supplements of potassium showed stronger associations.
  
  
  CONCLUSIONS
  Lasso is a relevant method in the analysis of databases with large number of exposures and can be recommended as an alternative to conventional strategies.
authors: Université Bordeaux, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, Bordeaux, France. marta.avalos@isped.u-bordeaux2.fr
authors: Marta
authors: M
authors: Avalos
authors: Nuria Duran
authors: ND
authors: Adroher
authors: Emmanuel
authors: E
authors: Lagarde
authors: Frantz
authors: F
authors: Thiessard
authors: Yves
authors: Y
authors: Grandvalet
authors: Benjamin
authors: B
authors: Contrand
authors: Ludivine
authors: L
authors: Orriols
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016022:Case-Control Studies; D002491:Central Nervous System Agents / Q000009:adverse effects; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D005260:Female; D005602:France; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D055553:Prescription Drugs / Q000009:adverse effects*; D012042:Registries; D012306:Risk; D055815:Young Adult
chemical_list: D002491:Central Nervous System Agents; D055553:Prescription Drugs