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• W2045367611 abstract "Mixed-effects models are commonly used to fit longitudinal or repeated measures data. A complication arises when the response is censored, for example, due to limits of quantification of the assay used. Although normal distributions are commonly assumed for random effects and residual errors, such assumptions make inferences vulnerable to outliers. The sensitivity to outliers and the need for heavy tailed distributions for random effects and residual errors motivate us to develop a likelihood-based inference for linear and nonlinear mixed effects models with cen- sored response (NLMEC/LMEC) based on the multivariate Student-t distribution. An ECM algorithm is developed for computing the maximum likelihood estimates for NLMEC/LMEC with the standard errors of the fixed effects and the exact like- lihood value as a by-product. The algorithm uses closed-form expressions at the E-step, that rely on formulas for the mean and variance of a truncated multivariate- t distribution. The proposed algorithm is implemented in the R package tlmec. It is applied to analyze longitudinal HIV viral load data in two recent AIDS studies. In addition, a simulation study is conducted to examine the performance of the proposed method and to compare it with the approach of Vaida and Liu (2009)." @default.
• W2045367611 created "2016-06-24" @default.
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• W2045367611 date "2013-01-01" @default.
• W2045367611 modified "2023-09-26" @default.
• W2045367611 title "Likelihood Based Inference Mixed-Effects Models with Censored Responses Using the Multivariate-t Distribution" @default.
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• W2045367611 doi "https://doi.org/10.5705/ss.2012.043" @default.
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