Flexible longitudinal linear mixed models for multiple censored responses data.

In biomedical studies and clinical trials, repeated measures are often subject to some upper and/or lower limits of detection. Hence, the responses are either left or right censored. A complication arises when more than one series of responses is repeatedly collected on each subject at irregular int...

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Publicado en:Statistics in Medicine Vol. 38; no. 6; pp. 1074 - 1103
Autores principales: Lachos, Victor H., A. Matos, Larissa, Castro, Luis M., Chen, Ming‐Hui, Chen, Ming-Hui
Formato: research Journal Article
Publicado: Wiley-Blackwell Mar2019
Acceso en línea:Ver este registro en EBSCOhost
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      jtl: Statistics in Medicine
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      dt: Mar2019
      vid: 38
      iid: 6
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1002/sim.8017
        NLM30421470
        134735605
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        atl: Flexible longitudinal linear mixed models for multiple censored responses data.
      aug:
        au:
          Lachos, Victor H.
          A. Matos, Larissa
          Castro, Luis M.
          Chen, Ming‐Hui
          Chen, Ming-Hui
        affil: Department of Statistics, University of Connecticut, Storrs Connecticut
      sug:
        subj:
          Linear Regression
          Prospective Studies
          Polymerase Chain Reaction
          Viral Load Statistics and Numerical Data
          Time Factors
          HIV Infections
          Multivariate Analysis
          Human
          Sensitivity and Specificity
          Algorithms
          Probability
          Validation Studies
          Comparative Studies
          Evaluation Research
          Multicenter Studies
          Scales
      ab: In biomedical studies and clinical trials, repeated measures are often subject to some upper and/or lower limits of detection. Hence, the responses are either left or right censored. A complication arises when more than one series of responses is repeatedly collected on each subject at irregular intervals over a period of time and the data exhibit tails heavier than the normal distribution. The multivariate censored linear mixed effect (MLMEC) model is a frequently used tool for a joint analysis of more than one series of longitudinal data. In this context, we develop a robust generalization of the MLMEC based on the scale mixtures of normal distributions. To take into account the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is considered. For this complex longitudinal structure, we propose an exact estimation procedure to obtain the maximum-likelihood estimates of the fixed effects and variance components using a stochastic approximation of the EM algorithm. This approach allows us to estimate the parameters of interest easily and quickly as well as to obtain the standard errors of the fixed effects, the predictions of unobservable values of the responses, and the log-likelihood function as a byproduct. The proposed method is applied to analyze a set of AIDS data and is examined via a simulation study.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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