PubMed · 16877570
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J Fox, J Weber, S Fidler. 2006. Primary HIV.. https://doi.org/10.1136/sti.2006.019877
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We propose a changepoint model for the analysis of longitudinal CD4 T-cell counts for HIV infected subjects following highly active antiretroviral treatment. The profile of CD4 counts for each subject follows a simple, 'broken stick' changepoint model, with random subject-specific parameters, including the changepoint. The model accounts for baseline covariates. The longitudinal CD4 records are censored at the time of the subject going off-study-treatment. This is a potentially informative drop-out mechanism, which we address by modelling it jointly with the CD4 count outcome. The drop-out model incorporates terms from the CD4 model, including the changepoint. The estimation is done in a Bayesian framework, with implementation via Markov chain Monte Carlo methods in the WinBUGS software. Model selection using DIC indicates that the data support the complex random changepoint and informative censoring model.
Large intra-individual variability in plasma levels may limit the interest of therapeutic drug monitoring based on a single determination. Indinavir concentrations were determined both in plasma and hair samples, and correlated with concomitant plasma HIV-RNA in 43 HIV-infected patients. In multivariate analysis, significant association was found between HIV-RNA below 50 copies/ml and indinavir concentrations in hair but not in plasma, suggesting that hair concentrations gave more extensive information on drug exposure than a single plasma sample.
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