Identification of a suitable matching procedure in health services research: Insights into a study for stroke patients

Background STROKE OWL is a quasi-experimental study using claims data from statutory health insurances in Germany to calculate the effect of case managers for stroke. Since there is no recruited control group, a suitable procedure needed to be identified to make the intervention effect measurable. Hence, the objective of this paper is to present an approach for comparing matching procedures before final data analyses take place. Methods We followed a four-step approach to identify an appropriate procedure on a partial dataset. First, we conducted a systematic review for identifying potential confounders of the study’s outcome. Afterwards we checked whether a matching procedure was able to balance the dataset with respect to the outcome, under the assumption that all relevant covariates including the intervention variable were balanced. Within the two last steps we checked covariate balances and remaining group sizes. Three matching procedures – coarsened exact matching, optimal full matching and propensity score matching – were tested. Results The coarsened exact matching was able to balance variables perfectly but on average lost >50% of the observations. Although optimal full and propensity score matching revealed some weaknesses concerning the variable balance, considerably more observations remained in the dataset. Based on the described approach and the external framework conditions of STROKE OWL the optimal full matching was chosen as matching procedure most suitable. Conclusion In summary, it was challenging to identify a suitable matching procedure for this study, since the detailed results of the different balance and group size checks varied.