Flexible modeling of complex HIV data

What is it about?

In biomedical studies, the analysis of longitudinal data based on Gaussian assumptions is common practice. Nevertheless, more often than not, the observed responses are naturally skewed, rendering the use of symmetric mixed effects models inadequate. In addition, it is also common in clinical assays that the patient’s responses are subject to some upper and/or lower quantification limit, depending on the diagnostic assays used for their detection. Furthermore, responses may also often present a nonlinear relation with some covariates, such as time. To address the aforementioned three issues, we consider a Bayesian semiparametric longitudinal censored model based on a combination of splines, wavelets, and the skew-normal distribution. The newly developed method is illustrated through simulated data and real data concerning AIDS/HIV viral loads.

Featured Image

Why is it important?

The newly developed method was applied to an HIV viral load dataset, which illustrated how the proposed model can produce, when compared to some of the existing alternatives, more accurate subject-specific estimated trajectories.

Perspectives