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Browsing Statistics by Subject "AIDS (Disease)"

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    Modelling acute HIV infection using longitudinally measured biomarker data including informative drop-out.
    (2009) Werner, Lise.; Mwambi, Henry G.
    Background. Numerous methods have been developed to model longitudinal data. In HIV/AIDS studies, HIV markers, CD4+ count and viral load are measured over time. Informative drop-out and the lower detection limit of viral load assays can bias the results and influence assumptions of the models. Objective The objective of this thesis is to describe the evolution of HIV markers in an HIV-1 subtype C acutely infected cohort of women from the CAPRISA 002: Acute Infection Study in Durban, South Africa. They were HIV treatment naive. Methods. Various linear mixed models were fitted to both CD4+ count and viral load, adjusting for repeated measurements, as well as including intercept and slope as random effects. The rate of change in each of the HIV markers was assessed using weeks post infection as both a linear effect and piecewise linear effects. Left-censoring of viral load was explored to account for missing data resulting from undetectable measurements falling below the lower detection limit of the assay. Informative drop- out was addressed by using a method of joint modelling in which a longitudinal and survival model were jointly linked using a latent Gaussian process. The progression of HIV markers were described and the effectiveness and usefulness of each modelling procedure was evaluated. Results. 62 women were followed for a median of 29 months post infection (IQR 20-39). Viral load increased sharply by 2.6 log copies/ml per week in the first 2 weeks of infection and decreased by 0.4 log copies/ml per week the next fortnight. It decreased at a slower rate thereafter. Similarly CD4+ count fell in the first 2 weeks by 4.4 square root cells/ul per week then recovered slightly only to decrease again. Left-censoring was unnecessary in this acute infection cohort as few viral load measures were below the detection limit and provided no improvement on model fit. Conclusion. Piecewise linear effects proved to be useful in quantifying the degree at which the HIV markers progress during the first few weeks of HIV infection, whereas modelling time as a linear effect was not very meaningful. Modelling HIV markers jointly with informative drop-out is shown to be necessary to account for the missing data incurred from participants leaving the study to initiate ARV treatment. In ignoring this drop-out, CD4+ count is estimated to be higher than what it actually is.
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    Modelling CD4+ count over time in HIV positive patients initiated on HAART in South Africa using linear mixed models.
    (2009) Yende, Nonhlanhla.; Mwambi, Henry G.
    HIV is among the highly infectious and pathogenic diseases with a high mortality rate. The spread of HIV is in uenced by several individual based epidemiological factors such as age, gender, mobility, sexual partner pro le and the presence of sexually transmitted infections (STI). CD4+ count over time provided the rst surrogate marker of HIV disease progression and is currently used for clinical management of HIV-positive patients. The CD4+ count as a key disease marker is repeatedly measured among those individuals who test HIV positive to monitor the progression of the disease since it is known that HIV/AIDS is a long wave event. This gives rise to what is commonly known as longitudinal data. The aim of this project is to determine if the patients' weight, baseline age, sex, viral load and clinic site, in uences the rate of change in CD4+ count over time. We will use data of patients who commenced highly active antiretroviral therapy (HAART) from the Center for the AIDS Programme of Research in South Africa (CAPRISA) in the AIDS Treatment Project (CAT) between June 2004 and September 2006, including two years of follow-up for each patient. Analysis was done using linear mixed models methods for longitudinal data. The results showed that larger increase in CD4+ count over time was observed in females and individuals who were younger. However, upon tting baseline log viral load in the model instead of the log viral at all visits was that, larger increase in CD4+ count was observed in females, individuals who were younger, had higher baseline log viral load and lower weight.