title: Performance of time-dependent propensity scores: a pharmacoepidemiology case study.
abstract: OBJECTIVE
  Pharmacoepidemiologic studies of acute effects of episodic exposures often must control for many time-dependent confounders. Marginal structural models permit this and provide unbiased estimates when confounders are on the causal pathway. However, if causal pathway confounding is minimal, analyses with time-dependent propensity scores, calculated for time periods defined by individual drug prescriptions, may have better efficiency. We justify time-dependent propensity scores and compare the performance of these methods in a case study from a previous investigation of the risk of medication toxicity death in current users of propoxyphene and hydrocodone, with both substantial time-dependent confounding and a large number of covariates.
  
  
  METHODS
  The cohort included Tennessee Medicaid enrollees who filled a qualifying study opioid prescription between 1992 and 2007. We identified 22 time-dependent covariates that accounted for most of the confounding in the original study. We compared analyses with all covariates in the regression model with those based on time-dependent propensity scores and those from marginal structural models.
  
  
  RESULTS
  We identified 489,008 persons with 1,771,295 propoxyphene and 4,088,754 hydrocodone prescriptions. The unadjusted hazard ratio (propoxyphene : hydrocodone) was 0.70 (95%CI, 0.46-1.07). Estimates from inclusion of all covariates in the model, time-dependent propensity score analysis with inverse probability of treatment weighting, and marginal structural models were 1.63 (1.04-2.57), 1.65 (1.01-2.72), and 1.64 (0.83-3.27), respectively. Findings varied little with use of alternative propensity score methods, time origin, or techniques for marginal structural model estimation.
  
  
  CONCLUSIONS
  Time-dependent propensity scores may be useful for pharmacoepidemiologic studies with time-varying exposures when causal pathway confounding is limited.
authors.affiliation: Department of Health Policy (WAR), Vanderbilt University School of Medicine, Nashville, TN, USA.
authors.forename: Wayne A
authors.initials: WA
authors.lastname: Ray
authors.forename: Qi
authors.initials: Q
authors.lastname: Liu
authors.forename: Bryan E
authors.initials: BE
authors.lastname: Shepherd
mesh_terms: D000328:Adult; D000368:Aged; D000701:Analgesics, Opioid / Q000009:adverse effects*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008484:Medicaid / Q000592:standards* / Q000706:statistics & numerical data; D008875:Middle Aged; D009026:Mortality / Q000639:trends; D017891:Pharmacoepidemiology / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D057216:Propensity Score; D013714:Tennessee / Q000453:epidemiology; D013997:Time Factors; D014481:United States / Q000453:epidemiology
chemical_list: D000701:Analgesics, Opioid
keywords: hydrocodone; marginal structural models; opioids; pharmacoepidemiology; propensity score; propoxyphene; time-dependent covariates