Birgit Fullerton Ph.D., Boris Pöhlmann Dipl.‐Inf. (F.H.), Robert Krohn Dipl.‐Demogr., John L. Adams Ph.D., M.S., Ferdinand M. Gerlach Prof. Dr. med., M.P.H., Antje Erler Dr. med, M.P.H.
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.
Administrative claims data from a German statutory health insurance fund covering the years 2004–2008.
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 (2). We further compared the effect estimates resulting from applying these different matching techniques in the evaluation of the 2.
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 2. All ‐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.
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.