title: Matching on the disease risk score in comparative effectiveness research of new treatments.
abstract: OBJECTIVE
  We use simulations and an empirical example to evaluate the performance of disease risk score (DRS) matching compared with propensity score (PS) matching when controlling large numbers of covariates in settings involving newly introduced treatments.
  
  
  METHODS
  We simulated a dichotomous treatment, a dichotomous outcome, and 100 baseline covariates that included both continuous and dichotomous random variables. For the empirical example, we evaluated the comparative effectiveness of dabigatran versus warfarin in preventing combined ischemic stroke and all-cause mortality. We matched treatment groups on a historically estimated DRS and again on the PS. We controlled for a high-dimensional set of covariates using 20% and 1% samples of Medicare claims data from October 2010 through December 2012.
  
  
  RESULTS
  In simulations, matching on the DRS versus the PS generally yielded matches for more treated individuals and improved precision of the effect estimate. For the empirical example, PS and DRS matching in the 20% sample resulted in similar hazard ratios (0.88 and 0.87) and standard errors (0.04 for both methods). In the 1% sample, PS matching resulted in matches for only 92.0% of the treated population and a hazard ratio and standard error of 0.89 and 0.19, respectively, while DRS matching resulted in matches for 98.5% and a hazard ratio and standard error of 0.85 and 0.16, respectively.
  
  
  CONCLUSIONS
  When PS distributions are separated, DRS matching can improve the precision of effect estimates and allow researchers to evaluate the treatment effect in a larger proportion of the treated population. However, accurately modeling the DRS can be challenging compared with the PS.
authors.affiliation: Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
authors.forename: Richard
authors.initials: R
authors.lastname: Wyss
authors.affiliation: The Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Alan R
authors.initials: AR
authors.lastname: Ellis
authors.affiliation: Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: M Alan
authors.initials: MA
authors.lastname: Brookhart
authors.affiliation: Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Michele
authors.initials: M
authors.lastname: Jonsson Funk
authors.affiliation: Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|CERobs Consulting, LLC, Chapel Hill, NC, USA.
authors.forename: Cynthia J
authors.initials: CJ
authors.lastname: Girman
authors.affiliation: Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Ross J
authors.initials: RJ
authors.lastname: Simpson
authors.affiliation: Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Til
authors.initials: T
authors.lastname: Stürmer
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001281:Atrial Fibrillation / Q000188:drug therapy / Q000401:mortality; D057186:Comparative Effectiveness Research / Q000379:methods*; D003198:Computer Simulation; D000069604:Dabigatran / Q000627:therapeutic use*; D005260:Female; D006801:Humans; D008297:Male; D009026:Mortality / Q000639:trends; D017891:Pharmacoepidemiology / Q000379:methods; D057216:Propensity Score; D020521:Stroke / Q000401:mortality / Q000517:prevention & control; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology; D014859:Warfarin / Q000627:therapeutic use*
chemical_list: D014859:Warfarin; D000069604:Dabigatran
keywords: comparative effectiveness research; confounding control; dabigatran; pharmacoepidemiology; prognostic score; propensity score; warfarin