title: Assessing the impact of propensity score estimation and implementation on covariate balance and confounding control within and across important subgroups in comparative effectiveness research.
abstract: OBJECTIVE
  Researchers are often interested in estimating treatment effects in subgroups controlling for confounding based on a propensity score (PS) estimated in the overall study population.
  
  
  OBJECTIVE
  To evaluate covariate balance and confounding control in sulfonylurea versus metformin initiators within subgroups defined by cardiovascular disease (CVD) history comparing an overall PS with subgroup-specific PSs implemented by 1:1 matching and stratification.
  
  
  METHODS
  We analyzed younger patients from a US insurance claims database and older patients from 2 Medicare (Humana Medicare Advantage, fee-for-service Medicare Parts A, B, and D) datasets. Confounders and risk factors for acute myocardial infarction were included in an overall PS and subgroup PSs with and without CVD. Covariate balance was assessed using the average standardized absolute mean difference (ASAMD).
  
  
  RESULTS
  Compared with crude estimates, ASAMD across covariates was improved 70%-94% for stratification for Medicare cohorts and 44%-99% for the younger cohort, with minimal differences between overall and subgroup-specific PSs. With matching, 75%-99% balance improvement was achieved regardless of cohort and PS, but with smaller sample size. Hazard ratios within each CVD subgroup differed minimally among PS and cohorts.
  
  
  CONCLUSIONS
  Both overall PSs and CVD subgroup-specific PSs achieved good balance on measured covariates when assessing the relative association of diabetes monotherapy with nonfatal myocardial infarction. PS matching generally led to better balance than stratification, but with smaller sample size. Our study is limited insofar as crude differences were minimal, suggesting that the new user, active comparator design identified patients with some equipoise between treatments.
authors.affiliation: *Department of Epidemiology, Merck Sharp & Dohme, North Wales, PA †Department of Epidemiology, University of North Carolina, Chapel Hill, NC ‡Department of Global Pharmacovigilance & Epidemiology, Bristol Meyers Squibb, Hopewell, NJ.
authors.forename: Cynthia J
authors.initials: CJ
authors.lastname: Girman
authors.forename: Mugdha
authors.initials: M
authors.lastname: Gokhale
authors.forename: Tzuyung Doug
authors.initials: TD
authors.lastname: Kou
authors.forename: Kimberly G
authors.initials: KG
authors.lastname: Brodovicz
authors.forename: Richard
authors.initials: R
authors.lastname: Wyss
authors.forename: Til
authors.initials: T
authors.lastname: Stürmer
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D015897:Comorbidity; D057186:Comparative Effectiveness Research / Q000379:methods*; D015986:Confounding Factors, Epidemiologic; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy*; D005260:Female; D006801:Humans; D007004:Hypoglycemic Agents / Q000009:adverse effects* / Q000627:therapeutic use; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D007348:Insurance, Health / Q000706:statistics & numerical data; D008297:Male; D008687:Metformin / Q000009:adverse effects; D008875:Middle Aged; D009203:Myocardial Infarction / Q000139:chemically induced*; D057216:Propensity Score; D012307:Risk Factors; D012737:Sex Factors; D013453:Sulfonylurea Compounds / Q000009:adverse effects; D014481:United States
chemical_list: D007004:Hypoglycemic Agents; D013453:Sulfonylurea Compounds; D008687:Metformin