title: The Comparison of Matching Methods Using Different Measures of Balance: Benefits and Risks Exemplified within a Study to Evaluate the Effects of German Disease Management Programs on Long-Term Outcomes of Patients with Type 2 Diabetes.
abstract: OBJECTIVE
  To present a case study on how to compare various matching methods applying different measures of balance and to point out some pitfalls involved in relying on such measures.
  
  
  METHODS
  Administrative claims data from a German statutory health insurance fund covering the years 2004-2008.
  
  
  METHODS
  We applied three different covariance balance diagnostics to a choice of 12 different matching methods used to evaluate the effectiveness of the German disease management program for type 2 diabetes (DMPDM2). We further compared the effect estimates resulting from applying these different matching techniques in the evaluation of the DMPDM2.
  
  
  RESULTS
  The choice of balance measure leads to different results on the performance of the applied matching methods. Exact matching methods performed well across all measures of balance, but resulted in the exclusion of many observations, leading to a change of the baseline characteristics of the study sample and also the effect estimate of the DMPDM2. All PS-based methods showed similar effect estimates. Applying a higher matching ratio and using a larger variable set generally resulted in better balance. Using a generalized boosted instead of a logistic regression model showed slightly better performance for balance diagnostics taking into account imbalances at higher moments.
  
  
  CONCLUSIONS
  Best practice should include the application of several matching methods and thorough balance diagnostics. Applying matching techniques can provide a useful preprocessing step to reveal areas of the data that lack common support. The use of different balance diagnostics can be helpful for the interpretation of different effect estimates found with different matching methods.
authors: Institute of General Practice, Johann Wolfgang Goethe-University, Frankfurt, Germany. fullerton@allgemeinmedizin.uni-frankfurt.de.
authors: Birgit
authors: B
authors: Fullerton
authors: AQUA Institute (Institute for Applied Quality Improvement and Research in Health Care), Göttingen, Germany.
authors: Boris
authors: B
authors: Pöhlmann
authors: AQUA Institute (Institute for Applied Quality Improvement and Research in Health Care), Göttingen, Germany.
authors: Robert
authors: R
authors: Krohn
authors: Department of Research and Evaluation, Kaiser Permanente Center for Effectiveness and Safety Research, Pasadena, CA.
authors: John L
authors: JL
authors: Adams
authors: Institute of General Practice, Johann Wolfgang Goethe-University, Frankfurt, Germany.
authors: Ferdinand M
authors: FM
authors: Gerlach
authors: Institute of General Practice, Johann Wolfgang Goethe-University, Frankfurt, Germany.
authors: Antje
authors: A
authors: Erler
mesh_terms: D000067575:Administrative Claims, Healthcare; D016022:Case-Control Studies; D002908:Chronic Disease; D057186:Comparative Effectiveness Research; D003924:Diabetes Mellitus, Type 2 / Q000628:therapy*; D019468:Disease Management; D005858:Germany; D006801:Humans; D007348:Insurance, Health / Q000458:organization & administration*; D015233:Models, Statistical; D019982:Organizational Case Studies; D017063:Outcome Assessment, Health Care / Q000379:methods; D057216:Propensity Score; D018570:Risk Assessment
keywords: Disease management; chronic care; diabetes; matching; measures of balance; propensity scores