Non-Parametric Fuzzy Regression Discontinuity Design in Modelling Viral Load Suppression Among People Living With HIV/AIDS (PLWHA)

Abstract

Adherence to antiretroviral therapy (ART) is a significant determinant of viral load suppression in HIV patients. There are inadequate statistical models that bring out the direct effects of ART on the suppression of HIV/AIDS. Traditional regression models address the general determinants of viral load suppression. Regression discontinuity designs, on the other hand, bring out the causal effects of ART on viral load suppression based on various thresholds. This study used the non-parametric fuzzy regression discontinuity design (FRDD) to model viral load suppression in PLWHA. The study began with developing a non-parametric FRDD, simulating the model to assess its performance, and applying the model to the Quality-of-Care dataset from Kaggle. The study focused on viral load suppression as the outcome variable, CD4 count and age as the running variables, gender, and whether a patient received counseling as additional covariates. The optimal thresholds were 40.5 years and 320 cubic millimeters for the CD4. There was an increasing negative treatment effect of ART on viral load suppression as the cutoff points for CD4 count increase. At the same time, there was an increasing negative treatment effect of ART on viral load suppression with increasing age. The compliance ratios for respondents increased with the negative increase in the treatment effect. Other analyses, such as the McCrary density test, bunching test, and manipulation test, indicated that the non-parametric fuzzy regression discontinuity design is effective in modeling viral load suppression.