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We analyse data on patient adherence to prescribed regimens and surrogate markers of clinical outcome for 168 human immunodeficiency virus infected patients treated with antiretroviral therapy. Data on patient adherence consisted of dose-timing measurements collected for an average of 12 months per patient via electronic monitoring of bottle opening events. We first discuss how such data can be presented to highlight suboptimal adherence patterns and between-patient differences, before introducing two novel methods by which such data can be statistically modelled. Correlations between adherence and subsequent measures of viral load and CD4+T-cell counts are then evaluated. We show that summary measures of short-term adherence, which incorporate pharmacokinetic and pharmacodynamic data on the monitored regimen, predict suboptimal trends in viral load and CD4+T-cell counts better than measures based on adherence data alone.

Original publication

DOI

10.1098/rsif.2005.0037

Type

Journal article

Journal

Journal of the Royal Society, Interface

Publication Date

09/2005

Volume

2

Pages

349 - 363

Addresses

Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College London, Norfolk Place, London W2 1PG, UK. neil.ferguson@ic.ac.uk

Keywords

Humans, HIV Infections, CD4 Lymphocyte Count, Treatment Outcome, Antiretroviral Therapy, Highly Active, Data Interpretation, Statistical, Risk Assessment, Risk Factors, Sensitivity and Specificity, Reproducibility of Results, Patient Compliance, Models, Biological, Computer Simulation, Outcome Assessment (Health Care), United States, Statistics as Topic