title: A Propensity-score-based Fine Stratification Approach for Confounding Adjustment When Exposure Is Infrequent.
abstract: When exposure is infrequent, propensity-score matching results in reduced precision because it discards a large proportion of unexposed patients. To our knowledge, the relative performance of propensity-score stratification in these circumstances has not been examined.
  
  
  
  Using an empirical example of the association of first trimester statin exposure (prevalence = 0.04%) with risk of congenital malformations and 1,000 simulated cohorts (n = 20,000) with eight combinations of exposure prevalence (0.5%, 1%, 5%, 10%) and outcome risk (3.5%, 10%), we compared four propensity-score-based approaches to confounding adjustment: (1) matching (1:1, 1:5, full), (2) stratification in 10, 50, and 100 strata by entire cohort propensity-score distribution, (3) stratification in 10, 50, and 100 strata by exposed group propensity-score distribution, (4) standardized mortality ratio (SMR) weighting. Weighted generalized linear models were used to derive effect estimates after weighting unexposed according to the distribution of the exposed in their stratum for the stratification approaches.
  
  
  
  In the empirical example, propensity-score stratification (cohort) approaches resulted in greater imbalances in covariate distributions between statin-exposed and unexposed compared with propensity-score stratification (exposed) and matching. In simulations, propensity-score stratification (exposed) resulted in smaller relative bias than the cohort approach with 10 and 50 strata, and greater precision than matching and SMR weighting at 0.5% and 1% exposure prevalence, but similar performance at 5% and 10%.
  
  
  
  For exposures with prevalence under 5%, propensity-score stratification with fine strata, based on the exposed group propensity-score distribution, produced the best results. For more common exposures, all approaches were equivalent.
authors.affiliation: From the aDivision of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital & Harvard Medical School, Boston, MA; bResearch Triangle Institute, Research Triangle Park, NC; cBoston University School of Public Health, Boston, MA; dDepartment of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA; and eDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
authors.forename: Rishi J
authors.initials: RJ
authors.lastname: Desai
authors.forename: Kenneth J
authors.initials: KJ
authors.lastname: Rothman
authors.forename: Brian T
authors.initials: BT
authors.lastname: Bateman
authors.forename: Sonia
authors.initials: S
authors.lastname: Hernandez-Diaz
authors.forename: Krista F
authors.initials: KF
authors.lastname: Huybrechts
mesh_terms: D000328:Adult; D015331:Cohort Studies; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D000013:Congenital Abnormalities / Q000453:epidemiology*; D005260:Female; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use*; D007231:Infant, Newborn; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D011247:Pregnancy; D011261:Pregnancy Trimester, First; D015995:Prevalence; D057216:Propensity Score
chemical_list: D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors