This study introduced the inverse probability weight and overlap weight by propensity score and how to test the balance and estimate the effect after weighting. Four R packages that can be used for propensity score weight analysis were introduced and compared.
Objective To compare the ability of three propensity score weighting methods to balance the covariates and the advantages and disadvantages to estimate the treatment effects when dealing with multiple treatment data under different sample sizes. Methods Monte Carlo simulation was used to generate data sets and the advantages and disadvantages of balancing covariates and estimating the treatment effects of three propensity score weighting methods, Logistic-IPTW, Logistic-OW and GBM-OW were compared. The evaluation index of covariate equilibrium level was the absolute standard mean difference. The evaluation indexes of effect estimation included the point estimate of treatment effect, root mean square error and confidence interval coverage. Results Compared with Logistic-IPTW and Logistic-OW, GBM-OW was better in effect estimation and had a smaller root mean square error in five scenarios where covariates were related to treatment factors and outcome variables with different varying degrees of complexity. In terms of covariate equilibrium, all three methods had good effects. GBM-OW method performed better when the overlap of propensity score distribution of multiple treatment data was relatively low and covariables had increasingly complex nonlinear relationships with treatment factors and outcome variables. Conclusion When dealing with multiple treatment data, GBM-OW method has advantages over the other two methods when there is nonlinearity and/or interaction between covariates and treatment factors and outcome variables. Using this method, the effect estimation is closer to the real value, which is a better choice.