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Type: Artigo de periódico
Title: Censored Mixed-effects Models For Irregularly Observed Repeated Measures With Applications To Hiv Viral Loads
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. © 2016 Sociedad de Estadística e Investigación Operativa
Editor: Springer New York LLC
Rights: fechado
Identifier DOI: 10.1007/s11749-016-0486-2
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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