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

摘要

HIV dynamic studies have contributed significantly to the understanding ofHIV pathogenesis and antiviral treatment strategies for AIDS patients.Establishing the relationship of virologic responses with clinical factors andcovariates during long-term antiretroviral (ARV) therapy is important to thedevelopment of effective treatments. Medication adherence is an importantpredictor of the effectiveness of ARV treatment, but an appropriate determinantof adherence rate based on medication event monitoring system (MEMS) data iscritical to predict virologic outcomes. The primary objective of this paper isto investigate the effects of a number of summary determinants of MEMSadherence rates on virologic response measured repeatedly over time inHIV-infected patients. We developed a mechanism-based differential equationmodel with consideration of drug adherence, interacted by virus susceptibilityto drug and baseline characteristics, to characterize the long-term virologicresponses 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 nonlinearmixed-effects modeling approach to assess how to efficiently use the MEMSadherence data for prediction of virologic response, and to evaluate thepredicting power of each summary metric of the MEMS adherence rates.