Propensity Score Stratification Using Support Vector Machine in HIV AIDS Case

Abstract

Many observational studies applied in the field of health, but Randomized Controlled Trials (RCT) is not always can be applied because it is directly related to human life. Therefore, a method is needed to solve the problem of bias as the effect of non-random observation and unbalanced covariates using propensity score (PS), it is Propensity Score Stratification (PSS). The purpose of PSS is to obtain a strata group that balance on each covariate. The PSS estimation of this research is using support vector machine (SVM). The case used in this research is opportunistic infection of HIV AIDS at Grati Health Center in Pasuruan district with the number of respondents are 150 patients. In the case of opportunistic infections HIV AIDS found that giving ARV therapy becomes confounding variable.The highest accuracy of PSS SVM on strata is 4, that is 64%. Estimation of treatment effects (ATE) gave results that the variable of ARV therapy is a variable that influence the opportunistic infections (Y) in HIV AIDS patients. The number of strata that reduce the largest bias is in the strata of 4 with the percent bias reduction (PBR) is 37.168% with the smallest standard error value is 0.075 and ATE value is 0.516.