Get the most from your data: a propensity score model comparison on real-life data

PURPOSE In the past, the propensity score has been in the middle of several discussions in terms of its abilities and limitations. With a comprehensive review and a practical example, this study examines the effect of propensity score analysis of real-life data and introduces a simple and effective clinical approach. MATERIALS AND METHODS After the authors reviewed current publications, they applied their insights to the data of a nonrandomized clinical trial in bariatric surgery. This study examined weight loss in 173 patients where 127 patients received Roux-en-Y gastric bypass surgery and 46 patients sleeve gastrectomy. Both groups underwent analysis in terms of their covariate distribution using Mann-Whitney U and χ (2) testing. Mean differences within excess weight loss in native data were examined with Student's t-test. Three propensity score models were defined and matching was performed. Covariate distribution and mean differences in excess weight loss were checked with Mann-Whitney U and χ (2) testing. RESULTS Native data implied a significant difference in excess weight loss. The propensity score models did not confirm this difference. All models proved that both surgical procedures were equal, due to their weight-loss induction. Covariate distribution improved after the matching procedure in terms of an equal distribution. CONCLUSION It seemed that a practical clinical approach with outcome-related covariates as a propensity score base is the ideal midpoint between an equal distribution in covariates and an acceptable loss of data. Nevertheless, propensity score models designed with clinical intent seemed to be absolutely suitable for overcoming heterogeneity in covariate distribution.