Markov Mixed Effects Modeling Using Electronic Adherence Monitoring Records Identifies Influential Covariates to HIV Preexposure Prophylaxis. (6th December 2016)

• Record Type: Journal Article
• Authors: Madrasi, Kumpal; Chaturvedula, Ayyappa; Haberer, Jessica E.; Sale, Mark; Fossler, Michael J.; Bangsberg, David; Baeten, Jared M.; Celum, Connie; Hendrix, Craig W.
• Is Part Of: Journal of clinical pharmacology. Volume 57:Number 5(2017)
• Page Start: 606
• Page End: 615
• Publication Date: 2016-12-06
• Subjects: adherence -- HIV -- Markov models -- medication event‐monitoring systems -- preexposure prophylaxis; Pharmacology -- Periodicals; Pharmacology -- Periodicals; Pharmacology, Clinical -- Periodicals; 615.1
• DOI: 10.1002/jcph.843
• Languages: English
• ISSNs: 0091-2700

Abstract

Abstract: Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed‐effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once‐daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV‐uninfected members of serodiscordant couples. One‐coin and first‐ to third‐order Markov models were fit to the data using NONMEM ® 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α = 0.05) and backward elimination (α = 0.001). Markov models better described the data than 1‐coin models. A third‐order Markov model gave the lowest OFV and AIC, but the simpler first‐order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher‐order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors.