OBJECTIVES: Estimating causal effects based on administrative health data comes with challenges due to the lack of randomization, unmeasured covariates and a wide range of variables with complex and unknown dependencies. To target these problems, many methodological approaches have been developed, addressing different of the challenges mentioned above. Researchers working with administrative health data lack guidelines for choosing the best approach for their analysis. While simulation studies aim to provide important guidance on this question, many of the simulation studies published to date do not take sufficient account of the complexity and character of real-world administrative health data. The aim of the study is therefore to conduct a simulation study based on real-world administrative health data to assess and compare approaches estimating causal effects.

METHODS: We compare six methods frequently used by researchers as well as methods recommended by previous simulation studies. We further estimate the nuisance parameters like the propensity score with regression models and an ensemble learner approach. We are interested in treatment effects on health care costs and utilization.

To generate data allowing for the complex data structures given in administrative health data, we resample the covariates from real-world administrative health data of three German sickness funds without modification and simulate a true treatment effect of choice based on the covariates. To replicate the problem of unobserved heterogeneity, a subset of covariates is omitted from analysis after simulation of the outcome variables.

RESULTS: Preliminary results of 72 simulation scenarios drawing data from 49.348 subjects suggest differences in performance between the methodological approaches used to estimate causal effects.

CONCLUSIONS: We run a simulation based on real-world administrative health data to compare approaches estimating causal treatment effects. Results will inform researchers and policy makers on which approach to take when estimating causal effects (e.g., when evaluating health care interventions) using administrative data.