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Updated: Nov 26, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Mixed-effects models for censored data with autoregressive errors.

Rommy C Olivari1, Aldo M Garay1, Victor H Lachos2

  • 1Department of Statistics, Federal University of Pernambuco, Recife, Brazil.

Journal of Biopharmaceutical Statistics
|December 14, 2020
PubMed
Summary
This summary is machine-generated.

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This study introduces a new statistical method for analyzing longitudinal data with detection limits, using mixed-effects models and autoregressive error structures. The approach provides accurate estimates and standard errors for complex longitudinal data analysis.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Longitudinal data analysis often involves measurements with detection limits.
  • Mixed-effects models are commonly used but require modifications for censored data.
  • Irregularly collected time-series data present unique analytical challenges.

Purpose of the Study:

  • To develop a likelihood-based approach for fitting mixed-effects models with censored observations (LMEC/NLMEC) and autoregressive error terms.
  • To implement an EM-type algorithm for maximum likelihood estimation and standard error calculation.
  • To address parameter space constraints using a reparameterization scheme.

Main Methods:

  • Fitting LMEC/NLMEC models with autoregressive order dependence.
Keywords:
Autoregressive AR(p) modelsEM algorithmHIV viral loadcensored datalinear/nonlinear mixed-effects models

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  • Utilizing an EM-type algorithm for parameter estimation.
  • Employing a reparameterization scheme to handle stationarity constraints.

    • Main Results:

    • The proposed EM-type algorithm effectively computes maximum likelihood estimates.
    • Standard errors for fixed effects and likelihood values are obtained as byproducts.
    • Simulation studies and a real AIDS case study demonstrate the method's performance.

    Conclusions:

    • The developed method provides a robust framework for analyzing longitudinal data with detection limits and autoregressive errors.
    • The new R package ARpLMEC facilitates the application of these advanced statistical techniques.
    • This approach enhances the analysis of complex biomedical and other time-series data.