Luis M. Castro, Víctor H. Lachos, Larissa A. Matos:
Censored mixed-effects models for irregularly observed repeated measures with applications to HIV viral loads
In some AIDS clinical trials, the HIV-1 RNA measurements are collected irregularly over time and are often subject to some upper and lower detection limits, depending on the quantification assays. Linear and nonlinear mixed-effects models, with modifications to accommodate censored observations, are routinely used to analyze this type of data (Vaida and Liu, 2009; Matos et al, 2013a). The paper presents a framework for fitting LMEC/NLMEC with response variables recorded at irregular intervals. To address the serial correlation among the within-subject errors, a damped exponential correlation structure is considered in the random error and an EM-type algorithm is developed for computing the maximum likelihood estimates, obtaining as a byproduct the standard errors of the fixed effects and the likelihood value. The proposed methods are illustrated with simulations and the analysis of two real AIDS case studies.
This preprint gave rise to the following definitive publication(s):
Luis M. CASTRO, Víctor H. LACHOS, Larissa A. MATOS: Censored mixed-effects models for irregularly observed repeated measures with applications to HIV viral loads. Test, vol. 25, 4, pp. 627-653, (2016).