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Multivariate longitudinal data analysis with censored and intermittent missing responses.

Tsung-I Lin1,2, Victor H Lachos3, Wan-Lun Wang4

  • 1Institute of Statistics, National Chung Hsing University, Taichung 402, Taiwan.

Statistics in Medicine
|May 10, 2018
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Summary
This summary is machine-generated.

This study introduces the MLMM-CM, a new method for analyzing complex longitudinal data with censored and missing values. It offers improved performance over traditional methods for multivariate longitudinal data analysis.

Keywords:
ECM algorithmHIV AIDS studycensored datamissing-data imputationtruncated multivariate normal distribution

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Clinical Trials

Background:

  • Multivariate linear mixed models (MLMM) are crucial for longitudinal data with multiple outcomes.
  • Censored measurements and intermittent missing values complicate MLMM analysis.
  • Existing methods struggle with combined censored and missing data in multivariate longitudinal studies.

Purpose of the Study:

  • To develop a generalized MLMM approach (MLMM-CM) for joint analysis of multivariate longitudinal data.
  • To address challenges posed by censored measurements and intermittent missing responses.
  • To provide a robust statistical framework for complex longitudinal data.

Main Methods:

  • Developed a computationally feasible expectation maximization-based procedure for maximum likelihood estimation.
  • Derived explicit asymptotic standard errors for fixed effects using an information-based method.
  • Validated the MLMM-CM approach using simulated data and a real-world AIDS clinical trial case study.

Main Results:

  • The proposed MLMM-CM method demonstrates superior performance compared to traditional MLMM approaches.
  • The expectation maximization procedure effectively handles censored and missing data.
  • The information-based method provides reliable asymptotic standard errors for fixed effects.

Conclusions:

  • The MLMM-CM offers a more satisfactory and robust approach for analyzing multivariate longitudinal data with censored and intermittent missing responses.
  • The developed methodology provides a valuable tool for biostatisticians and researchers in clinical trials.
  • This advancement enhances the analytical capabilities for complex longitudinal datasets.