title: Can statistical linkage of missing variables reduce bias in treatment effect estimates in comparative effectiveness research studies?
abstract: OBJECTIVE
  Missing data, particularly missing variables, can create serious analytic challenges in observational comparative effectiveness research studies. Statistical linkage of datasets is a potential method for incorporating missing variables. Prior studies have focused upon the bias introduced by imperfect linkage.
  
  
  METHODS
  This analysis uses a case study of hepatitis C patients to estimate the net effect of statistical linkage on bias, also accounting for the potential reduction in missing variable bias.
  
  
  RESULTS
  The results show that statistical linkage can reduce bias while also enabling parameter estimates to be obtained for the formerly missing variables.
  
  
  CONCLUSIONS
  The usefulness of statistical linkage will vary depending upon the strength of the correlations of the missing variables with the treatment variable, as well as the outcome variable of interest.
authors.affiliation: Optum Labs, One Main Street, 10th Floor, Cambridge, MA 02142, USA.
authors.forename: William
authors.initials: W
authors.lastname: Crown
authors.affiliation: Optum Labs, One Main Street, 10th Floor, Cambridge, MA 02142, USA.
authors.forename: Jessica
authors.initials: J
authors.lastname: Chang
authors.affiliation: Global Head HEOR Excellence, Novartis Pharma AG, 4056, Basel, Switzerland.
authors.forename: Melvin
authors.initials: M
authors.lastname: Olson
authors.affiliation: Novartis Pharmacueticals Corporation, One Health Plaza, East Hanover, NJ 07936-1080, USA.
authors.forename: Kristijan
authors.initials: K
authors.lastname: Kahler
authors.affiliation: Health Economics & Outcomes Research Optum, Inc., 200 E Randolph, Suite 5300, IL, 60601, USA.
authors.forename: Jason
authors.initials: J
authors.lastname: Swindle
authors.affiliation: Health Economics & Outcomes Research MN002-0258, 12125 Technology Drive, Eden Prairie, MN 55344, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Buzinec
authors.affiliation: Division of Health Care Policy & Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
authors.forename: Nilay
authors.initials: N
authors.lastname: Shah
authors.affiliation: Mayo College of Medicine, Division of Healthcare & Medicine, 200 First St SW, Rochester, MN 55905, USA.
authors.forename: Bijan
authors.initials: B
authors.lastname: Borah
mesh_terms: D015982:Bias; D057186:Comparative Effectiveness Research / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006526:Hepatitis C / Q000453:epidemiology*; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D012189:Retrospective Studies
keywords: claims analysis; comparative effectiveness research; electronic medical records; missing variable bias; retrospective database studies; statistical linkage