2021
DOI: 10.1177/09622802211046387
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A semiparametric mixed-effects model for censored longitudinal data

Abstract: In longitudinal studies involving laboratory-based outcomes, repeated measurements can be censored due to assay detection limits. Linear mixed-effects (LMEs) models are a powerful tool to model the relationship between a response variable and covariates in longitudinal studies. However, the linear parametric form of linear mixed-effect models is often too restrictive to characterize the complex relationship between a response variable and covariates. More general and robust modeling tools, such as nonparametri… Show more

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Cited by 7 publications
(8 citation statements)
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“…where 𝓁( θ) corresponds to the logarithm of the observed likelihood function 𝓁(𝜃|y), p * is the total number of parameters in the model, and N denotes the sample size. Note that other authors, such as Ibacache-Pulgar et al 21 (see also Mattos et al 39 ), use the logarithm of the penalized likelihood function instead of the observed one. In our case, we compare both approaches, and the conclusions remain the same.…”
Section: 𝜈mentioning
confidence: 99%
See 4 more Smart Citations
“…where 𝓁( θ) corresponds to the logarithm of the observed likelihood function 𝓁(𝜃|y), p * is the total number of parameters in the model, and N denotes the sample size. Note that other authors, such as Ibacache-Pulgar et al 21 (see also Mattos et al 39 ), use the logarithm of the penalized likelihood function instead of the observed one. In our case, we compare both approaches, and the conclusions remain the same.…”
Section: 𝜈mentioning
confidence: 99%
“…This section further analyzes the A5055 dataset described in Acosta et al 45 to illustrate our method. This dataset has been previously analyzed by Mattos et al 39 considering a Gaussian distribution as the distribution of the random effects and error terms. Thus, for comparison purposes, we fitted the same model as in Mattos et al, 39 that is: where y ij denotes the log 10 transformation of the viral load for the ith subject at time…”
Section: A5055 Datamentioning
confidence: 99%
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