Influence assessment in censored mixed-effects models using the multivariate Student's-t distribution

Autor(es)

Matos, Larissa A. ^[1] ; Bandyopadhyay, Dipankar ^[2] ; Castro, Luis M. ^[3] ; Lachos, Victor H. ^[1]

Afiliação do(s) autor(es)

^[1] IMECC UNICAMP, Dept Estat, Sao Paulo - Brazil

^[2] Univ Minnesota, Div Biostat, Minneapolis, MN 55455 - USA

^[3] Univ Concepcion, Dept Estat, Concepcion - Chile

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF MULTIVARIATE ANALYSIS; v. 141, p. 104-117, OCT 2015.
• Citações Web of Science: 3

Resumo

In biomedical studies on HIV RNA dynamics, viral loads generate repeated measures that are often subjected to upper and lower detection limits, and hence these responses are either left- or right-censored. Linear and non-linear mixed-effects censored (LMEC/NLMEC) models are routinely used to analyze these longitudinal data, with normality assumptions for the random effects and residual errors. However, the derived inference may not be robust when these underlying normality assumptions are questionable, especially the presence of outliers and thick-tails. Motivated by this, Matos et al. (2013) recently proposed an exact EM-type algorithm for LMEC/NLMEC models using a multivariate Student's-t distribution, with closed-form expressions at the E-step. In this paper, we develop influence diagnostics for LMEC/NLMEC models using the multivariate Student's-t density, based on the conditional expectation of the complete data log-likelihood. This partially eliminates the complexity associated with the approach of Cook (1977, 1986) for censored mixed-effects models. The new methodology is illustrated via an application to a longitudinal HIV dataset. In addition, a simulation study explores the accuracy of the proposed measures in detecting possible influential observations for heavy-tailed censored data under different perturbation and censoring schemes. (C) 2015 Elsevier Inc. All rights reserved. (AU)

• Processo FAPESP: 11/22063-9 - Aplicações das distribuições de misturas da escala skew-normal em modelos de análise fatorial
• Beneficiário: Larissa Avila Matos
• Linha de fomento: Bolsas no Brasil - Doutorado
• Processo FAPESP: 14/02938-9 - Estimação e diagnóstico em modelos de efeitos mistos para dados censurados usando misturas de escala skew-normal
• Beneficiário: Víctor Hugo Lachos Dávila
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 12/19445-0 - Modelagem flexível de modelos longitudinais complexos usando distribuições skew-elípticas
• Beneficiário: Víctor Hugo Lachos Dávila
• Linha de fomento: Auxílio à Pesquisa - Pesquisador Visitante - Internacional