In some acquired immunodeficiency syndrome (AIDS) clinical trials, the
human immunodeficiency virus-1 ribonucleic acid measurements are collected irreg-
ularly 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 ana-
lyze this type of data (Vaida and Liu, J Comput Graph Stat 18:797–817, 2009; Matos et
al., Comput Stat Data Anal 57(1):450–464, 2013a). This 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.

EB00000004181K

Available

• Censored mixed-effects models for irregularly observed repeated measures with applications to HIV viral loads