title: Confronting "confounding by health system use" in Medicare Part D: comparative effectiveness of propensity score approaches to confounding adjustment.
abstract: OBJECTIVE
  Under Medicare Part D, patient characteristics influence plan choice, which in turn influences Part D coverage gap entry. We compared predefined propensity score (PS) and high-dimensional propensity score (hdPS) approaches to address such "confounding by health system use" in assessing whether coverage gap entry is associated with cardiovascular events or death.
  
  
  METHODS
  We followed 243,079 Medicare patients aged 65+ years with linked prescription, medical, and plan-specific data in 2005-2007. Patients reached the coverage gap and were followed until an event or year's end. Exposed patients were responsible for drug costs in the gap; unexposed patients (patients with non-Part D drug insurance and Part D patients receiving a low-income subsidy) received financial assistance. Exposed patients were 1:1 PS-matched or hdPS-matched to unexposed patients. The PS model included 52 predefined covariates; the hdPS model added 400 empirically identified covariates. Hazard ratios for death and any of five cardiovascular outcomes were compared. In sensitivity analyses, we explored residual confounding using only low-income subsidy patients in the unexposed group.
  
  
  RESULTS
  In unadjusted analyses, exposed patients had no greater hazard of death (HR = 1.00; 95%CI, 0.84-1.20) or other outcomes. PS-matched (HR = 1.29; 0.99-1.66) and hdPS-matched (HR = 1.11; 0.86-1.42) analyses showed elevated but non-significant hazards of death. In sensitivity analyses, the PS analysis showed a protective effect (HR = 0.78; 0.61-0.98), whereas the hdPS analysis (HR = 1.06; 0.82-1.37) confirmed the main hdPS findings.
  
  
  CONCLUSIONS
  Although the PS-matched analysis suggested elevated but non-significant hazards of death among patients with no financial assistance during the gap, the hdPS analysis produced lower estimates that were stable across sensitivity analyses.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA. jpolinski@partners.org
authors.forename: Jennifer M
authors.initials: JM
authors.lastname: Polinski
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schneeweiss
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Glynn
authors.forename: Joyce
authors.initials: J
authors.lastname: Lii
authors.forename: Jeremy A
authors.initials: JA
authors.lastname: Rassen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002318:Cardiovascular Diseases / Q000188:drug therapy / Q000191:economics / Q000401:mortality*; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D004363:Drug Utilization / Q000191:economics / Q000639:trends; D005260:Female; D006801:Humans; D019458:Insurance Coverage / Q000191:economics / Q000706:statistics & numerical data / Q000639:trends; D008297:Male; D054524:Medicare Part D / Q000191:economics / Q000706:statistics & numerical data / Q000639:trends; D009026:Mortality / Q000639:trends; D011203:Poverty / Q000706:statistics & numerical data; D055553:Prescription Drugs / Q000191:economics; D057216:Propensity Score; D011446:Prospective Studies; D012959:Socioeconomic Factors; D014481:United States
chemical_list: D055553:Prescription Drugs