Propensity score based data analysis

For some time, propensity score (PS) based methods have been frequently applied in the analysis of observational and registry data. The PS is the conditional probability of a certain treatment given patient’s covariates. PS methods are used to eliminate imbalances in baseline covariate distributions between treatment groups and permit to estimate marginal effects. The package nonrandom is a tool for a comprehensive data analysis using stratification, matching and covariate adjustment by PS. Several functions are implemented, starting from the selection of the PS model up to estimating the PS based treatment effect. Before estimating the PS, knowledge about which covariates should be included in the PS model is needed. relative.effect() provides the opportunity to investigate the extent to which a covariate confounds the treatmentoutcome relationship. pscore() estimates the PS and plot.pscore() offers a graphical presentation of the PS distribution. Stratification and matching by PS are directly implemented in ps.makestrata() and ps.match(). To check covariate balance between treatment groups, statistical tests or standardized differences are given in ps.balance(). In addition, dist.plot() and plot.stdf() provide a graphical balance check. Finally, the PS based treatment effect can be estimated in ps.estimate(). This function also offers a comparison to regression based estimates. A special case of regression based analysis may be covariate adjustment by PS. All functions can be applied separately and combined. Additionally, it is possible to apply all functions repeatedly to decide which analysis strategy is most suitable. Print and summary functions are available for the most implemented functions. There are two real data examples to illustrate the application of nonrandom. In the first data example, quality of life is investigated in breast cancer patients in an observational treatment study of the German Breast Cancer Study Group (GBSG). The second data example deals with lower respiratory tract infections (LRTI) in infants and children in the observational study Pri.DE (Pediatric Respiratory Infection, Deutschland) in Germany.