Influence diagnostics in linear and nonlinear mixed-effects models with censored data

dc.creatorMatos, LA
dc.creatorLachos, VH
dc.creatorBalakrishnan, N
dc.creatorLabra, FV
dc.date2013
dc.dateJAN
dc.date2014-07-30T14:33:30Z
dc.date2015-11-26T16:33:29Z
dc.date2014-07-30T14:33:30Z
dc.date2015-11-26T16:33:29Z
dc.date.accessioned2018-03-28T23:15:21Z
dc.date.available2018-03-28T23:15:21Z
dc.identifierComputational Statistics & Data Analysis. Elsevier Science Bv, v. 57, n. 1, n. 450, n. 464, 2013.
dc.identifier0167-9473
dc.identifierWOS:000310403700034
dc.identifier10.1016/j.csda.2012.06.021
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/60178
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/60178
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1270866
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionHIV RNA viral load measures are often subjected to some upper and lower detection limits depending on the quantification assays, and consequently the responses are either left or right censored. Linear and nonlinear mixed-effects models, with modifications to accommodate censoring (LMEC and NLMEC), are routinely used to analyze this type of data. Recently, Vaida and Liu (2009) proposed an exact EM-type algorithm for LMEC/NLMEC, called the SAGE algorithm (Meng and Van Dyk, 1997), that uses closed-form expressions at the E-step, as opposed to Monte Carlo simulations. Motivated by this algorithm, we propose here an exact ECM algorithm (Meng and Rubin, 1993) for LMEC/NLMEC, which enables us to develop local influence analysis for mixed-effects models on the basis of conditional expectation of the complete-data log-likelihood function. This is because the observed data log-likelihood function associated with the proposed model is somewhat complex which makes it difficult to directly apply the approach of Cook (1977, 1986). Some useful perturbation schemes are also discussed. Finally, the results obtained from the analyses of two HIV AIDS studies on viral loads are presented to illustrate the newly developed methodology. (C) 2012 Elsevier B.V. All rights reserved.
dc.description57
dc.description1
dc.description450
dc.description464
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationComputational Statistics & Data Analysis
dc.relationComput. Stat. Data Anal.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectCensored data
dc.subjectHIV viral load
dc.subjectEM algorithm
dc.subjectInfluential observations
dc.subjectLinear and nonlinear mixed models
dc.subjectLocal Influence
dc.subjectIncomplete-data
dc.subjectEm
dc.titleInfluence diagnostics in linear and nonlinear mixed-effects models with censored data
dc.typeArtículos de revistas


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