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Objectives: To compare the ability of logistic regression and generalized boosted modelling (GBM) to estimate treatment effects and balance covariates in inverse probability of treatment weighting (IPTW) and to explore the independent impact of different types of medical insurance on drug costs of outpatients with coronary heart disease based on this method. Methods: This study was used to evaluate the performance of logistic regression and GBM in IPTW under a Monte Carlo study with the simulated design of linear and nonlinear correlations between treatment variable and covariates of different sample sizes (n=500, 2000). The assessment indicators included average standardized absolute mean difference (ASAM), point estimation, bias, relative bias, standard error, mean square error, 95% confidence interval coverage rate, distribution of weights and an empirical study of outpatients with coronary heart disease was carried out after simulation. Results: The simulations show that GBM in propensity score weighting is superior to logistic regression in the lower bias and mean square error and it achieves better covariate balance, especially in nonlinear conditioning models. And in this case study. It’s found that GBM in IPTW has better ability to balance the confounding factors compared with logistic regression. The weighted results show that the drug costs of outpatients with coronary heart disease of Urban Employee Basic Medical Insurance increase by 256.35 Yuan on average compared with those of Urban-Rural Resident Basic Medical Insurance. Conclusion: It may be better to control confounding factors in case of the unknown relationship between the treatment variable and covariates by IPTW with GBM. There is still a certain gap in drug costs among different types of medical insurance for patients with coronary heart disease according to this study, which provides a reasonable scientific basis for the optimal allocation of medical insurance system and health resources in coronary heart disease.

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Logistic Regression and Generalized Boosted Modelling in Inverse Probability of Treatment Weighting: A Simulation and Case Study of Outpatients with Coronary Heart Disease