title: Outcome-adaptive lasso: Variable selection for causal inference.
abstract: Methodological advancements, including propensity score methods, have resulted in improved unbiased estimation of treatment effects from observational data. Traditionally, a "throw in the kitchen sink" approach has been used to select covariates for inclusion into the propensity score, but recent work shows including unnecessary covariates can impact both the bias and statistical efficiency of propensity score estimators. In particular, the inclusion of covariates that impact exposure but not the outcome, can inflate standard errors without improving bias, while the inclusion of covariates associated with the outcome but unrelated to exposure can improve precision. We propose the outcome-adaptive lasso for selecting appropriate covariates for inclusion in propensity score models to account for confounding bias and maintaining statistical efficiency. This proposed approach can perform variable selection in the presence of a large number of spurious covariates, that is, covariates unrelated to outcome or exposure. We present theoretical and simulation results indicating that the outcome-adaptive lasso selects the propensity score model that includes all true confounders and predictors of outcome, while excluding other covariates. We illustrate covariate selection using the outcome-adaptive lasso, including comparison to alternative approaches, using simulated data and in a survey of patients using opioid therapy to manage chronic pain.
authors.affiliation: Biostatistics Unit, Group Health Research Institute, Seattle, Washington, U.S.A.|Department of Biostatistics, University of Washington, School of Public Health, Seattle, Washington, U.S.A.
authors.forename: Susan M
authors.initials: SM
authors.lastname: Shortreed
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, U.S.A.|Department of Statistics, The Wharton School, University of Pennsylvania Center for Pharmacoepidemiology Research and Training, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.
authors.forename: Ashkan
authors.initials: A
authors.lastname: Ertefaie
mesh_terms: D000701:Analgesics, Opioid / Q000627:therapeutic use; D015982:Bias; D001699:Biometry; D059350:Chronic Pain / Q000188:drug therapy; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care; D057216:Propensity Score
chemical_list: D000701:Analgesics, Opioid
keywords: Comparative effectiveness; Model selection; Observational studies; Propensity score