  Assessment of circulating CD4 count change over time in HIV-infected subjects
on antiretroviral therapy (ART) is a central component of disease monitoring.
The increasing number of HIV-infected subjects starting therapy and the limited
capacity to support CD4 count testing within resource-limited settings have
fueled interest in identifying correlates of CD4 count change such as total
lymphocyte count, among others. The application of modeling techniques will be
essential to this endeavor due to the typically nonlinear CD4 trajectory over
time and the multiple input variables necessary for capturing CD4 variability.
We propose a prediction-based classification approach that involves first stage
modeling and subsequent classification based on clinically meaningful
thresholds. This approach draws on existing analytical methods described in the
receiver operating characteristic curve literature while presenting an
extension for handling a continuous outcome. Application of this method to an
independent test sample results in greater than 98% positive predictive value
for CD4 count change. The prediction algorithm is derived based on a cohort of
$n=270$ HIV-1 infected individuals from the Royal Free Hospital, London who
were followed for up to three years from initiation of ART. A test sample
comprised of $n=72$ individuals from Philadelphia and followed for a similar
length of time is used for validation. Results suggest that this approach may
be a useful tool for prioritizing limited laboratory resources for CD4 testing
after subjects start antiretroviral therapy.
