Propensity score stratification using bootstrap aggregating classification trees analysis

INTRODUCTION: Observational research in the field of health often does not conduct randomized controlled trials on research subjects. A non-random selection process on research subjects can result in a biased treatment effect due to an imbalance between the treatment and control groups. METHODS: The problem of bias effects can be dealt with by reducing the bias in the confounding variable using the propensity score method. Estimation of propensity score can use machine learning method with a classification tree analysis approach. The resulting single classification tree model is still unstable if there is a slight change in learning data. Therefore, the ensemble method is applied which is bootstrap aggregating the classification tree as a tool to improve the stability and predictive power of the classification tree. RESULTS: This study aims to determine the effect of giving treatment antiretroviral therapy and counseling to opportunistic infections in HIV AIDS patients. The result of propensity score stratification analysis using bootstrap aggregating classification trees analysis is able to reduce the bias by 89.54%, using 5 strata and having a balanced covariate in each stratum. CONCLUSION: Testing the effect of treatment shows that there is a significant effect of giving antiretroviral therapy and counseling to opportunistic infections in HIV AIDS patients.