title: Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research.
abstract: OBJECTIVE
  Examining covariate balance is the prescribed method for determining the degree to which propensity score methods should be successful at reducing bias. This study assessed the performance of various balance measures, including a proposed balance measure based on the prognostic score (similar to a disease risk score), to determine which balance measures best correlate with bias in the treatment effect estimate.
  
  
  METHODS
  The correlations of multiple common balance measures with bias in the treatment effect estimate produced by weighting by the odds, subclassification on the propensity score, and full matching on the propensity score were calculated. Simulated data were used, based on realistic data settings. Settings included both continuous and binary covariates and continuous covariates only.
  
  
  RESULTS
  The absolute standardized mean difference (ASMD) in prognostic scores, the mean ASMD (in covariates), and the mean t-statistic all had high correlations with bias in the effect estimate. Overall, prognostic scores displayed the highest correlations with bias of all the balance measures considered. Prognostic score measure performance was generally not affected by model misspecification, and the prognostic score measure performed well under a variety of scenarios.
  
  
  CONCLUSIONS
  Researchers should consider using prognostic score-based balance measures for assessing the performance of propensity score methods for reducing bias in nonexperimental studies.
authors.affiliation: Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 North Broadway, Hampton House, Baltimore, MD 21205, USA. estuart@jhsph.edu
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Stuart
authors.forename: Brian K
authors.initials: BK
authors.lastname: Lee
authors.forename: Finbarr P
authors.initials: FP
authors.lastname: Leacy
mesh_terms: D015984:Causality; D057186:Comparative Effectiveness Research / Q000706:statistics & numerical data*; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D035061:Control Groups; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011379:Prognosis; D057216:Propensity Score; D016896:Treatment Outcome
keywords: Causal inference; Confounding; Disease risk score; Matching methods; Nonexperimental study; Propensity score diagnostics