A dynamic Bayesian nonlinear mixed-effects model of HIV response incorporating medication adherence, drug resistance and covariates

TL;DR

This paper introduces a Bayesian nonlinear mixed-effects model that integrates medication adherence, drug resistance, and covariates to predict long-term HIV virologic responses, enhancing understanding of treatment effectiveness.

Contribution

It develops a novel differential equation model combined with Bayesian methods to analyze MEMS adherence data and predict HIV virologic outcomes more accurately.

Findings

Model effectively predicts virologic response using adherence data.

Certain adherence metrics significantly improve prediction accuracy.

The approach integrates multiple factors for comprehensive HIV treatment analysis.

Abstract

HIV dynamic studies have contributed significantly to the understanding of HIV pathogenesis and antiviral treatment strategies for AIDS patients. Establishing the relationship of virologic responses with clinical factors and covariates during long-term antiretroviral (ARV) therapy is important to the development of effective treatments. Medication adherence is an important predictor of the effectiveness of ARV treatment, but an appropriate determinant of adherence rate based on medication event monitoring system (MEMS) data is critical to predict virologic outcomes. The primary objective of this paper is to investigate the effects of a number of summary determinants of MEMS adherence rates on virologic response measured repeatedly over time in HIV-infected patients. We developed a mechanism-based differential equation model with consideration of drug adherence, interacted by virus susceptibility to drug and baseline characteristics, to characterize the long-term virologic responses after initiation of therapy. This model fully integrates viral load, MEMS adherence, drug resistance and baseline covariates into the data analysis. In this study we employed the proposed model and associated Bayesian nonlinear mixed-effects modeling approach to assess how to efficiently use the MEMS adherence data for prediction of virologic response, and to evaluate the predicting power of each summary metric of the MEMS adherence rates.