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

Larissa A. Matos (), Luis M. Castro () and Víctor H. Lachos ()
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Larissa A. Matos: Universidade Estadual de Campinas
Luis M. Castro: Universidad de Concepción
Víctor H. Lachos: Universidade Estadual de Campinas

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2016, vol. 25, issue 4, No 3, 627-653

Abstract: Abstract In some acquired immunodeficiency syndrome (AIDS) clinical trials, the human immunodeficiency virus-1 ribonucleic acid 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, 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.

Keywords: Censored data; EM algorithm; HIV viral load; Irregularly observed data; Linear/nonlinear mixed models; 62J02; 62J05 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11749-016-0486-2

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