title: An updated method for risk adjustment in outcomes research.
abstract: OBJECTIVE
  To demonstrate why meta-analytic methods need modification before they can be used to aggregate rates or effect sizes in outcomes research, under the constraint of no common underlying effect or rate.
  
  
  METHODS
  Studies are presented that require different types of risk adjustment. First, we demonstrate using rates that external risk adjustment through standardization can be achieved using modified meta-analytic methods, but only with a model that allows input of user-defined weights. Next, we extend these observations to internal risk adjustment of comparative effect sizes.
  
  
  RESULTS
  We show that this procedure produces identical results to conventional age standardization if a rate is being standardized for age. We also demonstrate that risk adjustment of effect sizes can be achieved with this modified method but cannot be done using standard meta-analysis.
  
  
  CONCLUSIONS
  We conclude that this method allows risk adjustment to be performed in situations in which currently the fixed- or random-effects methods of meta-analysis are inappropriately used. The latter should be avoided when the underlying aim is risk adjustment rather than meta-analysis.
authors.affiliation: School of Population Health, University of Queensland, Brisbane, Australia. Electronic address: sardoi@gmx.net.
authors.forename: Suhail A R
authors.initials: SA
authors.lastname: Doi
authors.affiliation: School of Population Health, University of Queensland, Brisbane, Australia.
authors.forename: Jan J
authors.initials: JJ
authors.lastname: Barendregt
authors.affiliation: School of Population Health, University of Queensland, Brisbane, Australia.
authors.forename: Chalapati
authors.initials: C
authors.lastname: Rao
mesh_terms: D000367:Age Factors; D006801:Humans; D015201:Meta-Analysis as Topic; D008962:Models, Theoretical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D020379:Risk Adjustment / Q000379:methods*
keywords: age standardization; burden of disease; fixed-effects model; meta-analysis; population standardization; quality-effects model; random-effects model