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Bayesian latent-class mixed-effect hybrid models for dyadic longitudinal data with non-ignorable dropouts.

Jaeil Ahn1, Suyu Liu, Wenyi Wang

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, U.S.A.; Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, U.S.A.

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Analyzing longitudinal dyadic data with non-ignorable dropouts is complex. This study introduces a novel mixed-effects hybrid model to effectively handle these challenges in statistical analysis.

Keywords:
DyadicLatent classLongitudinalMixed-effectNon-ignorable missingness

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal dyadic data analysis presents challenges due to complex correlations within and between dyads.
  • Non-ignorable dropouts further complicate the analysis of such data.

Purpose of the Study:

  • To propose a novel approach for analyzing longitudinal dyadic data with non-ignorable dropouts.
  • To address the complexities of within- and between-dyad correlations and missing data patterns.

Main Methods:

  • A mixed-effects hybrid model is developed, factorizing the joint distribution into random effects, dropout process, and measurement process.
  • The conditional dropout process is modeled using a discrete survival model.
  • The conditional measurement process is modeled using a latent-class pattern-mixture model, incorporating actor, partner, and dyad-specific random effects.
  • A latent-dropout-class approach is employed to manage numerous missing data patterns.

Main Results:

  • The proposed method effectively analyzes longitudinal dyadic data with non-ignorable dropouts.
  • Simulation studies demonstrate the performance of the developed statistical approach.
  • The method was successfully applied to a prostate cancer trial dataset.

Conclusions:

  • The proposed mixed-effects hybrid model provides a robust framework for analyzing longitudinal dyadic data with non-ignorable dropouts.
  • This approach accounts for dyadic interdependence and complex missing data patterns.
  • The method shows practical utility in real-world clinical trial data analysis.