Perbandingan Propensity Score Stratification dan Propensity Score Matching dengan Pendekatan Multivariate Adaptive Regression Spline

Abstract

Research on complications of Diabetes Mellitus (DM) is multifactorial, where the risk factors causing DM complications are interrelated, leading to confounding bias, which results in inaccurate research findings. Confounding bias can be reduced using the propensity score method. This study aims to compare the performance of the Propensity Score Stratification (PSS) and Propensity Score Matching (PSM) methods with the Multivariate Adaptive Regression Spline (MARS) approach in estimating treatment effects on DM complication cases. The data used is the medical records of type-2 DM patients at Hospital X. The results showed that the PSS method with the MARS approach is not suitable for small data sets, as it can lead to treatment or control groups lacking members, making it impossible to calculate the p-value in balance testing or the Percent Bias Reduction (PBR). The estimated Average Treatment Effect (ATE) using the PSS method was 0.487 with a PBR of 35.1%, whereas the estimated Average Treatment for Treated (ATT) using the PSM method was 0.531 with a PBR of 99.46%. These PBR values indicate that the best method for estimating treatment effects and the one that can reduce the most bias in this case is the PSM method with MARS. The analysis also showed that serum uric acid levels significantly affect the peripheral diabetic neuropathy (PDN) status of DM patients.