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Bayesian analysis of longitudinal dyadic data with informative missing data using a dyadic shared-parameter model.

Jaeil Ahn1, Satoshi Morita2, Wenyi Wang3

  • 11 Department of Biostatistics, Georgetown University, Washington, DC, USA.

Statistical Methods in Medical Research
|June 21, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel dyadic shared-parameter model for analyzing longitudinal dyadic data with ordinal outcomes and missing data. The method effectively handles complex correlations and informative missingness in dyadic studies.

Keywords:
Dyadicintermittent missinglongitudinal studynon-ignorable missingnesssensitivity analysisshared-parameter

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Analyzing longitudinal dyadic data presents challenges due to complex correlations and missing data.
  • Existing methods may not adequately address within-dyad interdependence and informative missingness simultaneously.

Purpose of the Study:

  • To propose a novel dyadic shared-parameter model for longitudinal dyadic data with ordinal outcomes.
  • To effectively handle informative intermittent missing data and dropouts within dyadic structures.

Main Methods:

  • A dyadic shared-parameter model incorporating a proportional odds model for longitudinal measurements.
  • Actor-partner interdependence effects and dyad-specific random effects are utilized.
  • A transition model is employed for informative missing data and dropouts, sharing random effects with the measurement model.

Main Results:

  • Extensive simulation studies demonstrate the performance of the proposed method.
  • Sensitivity analyses were conducted to assess the impact of potential misspecification of the missing data mechanism.

Conclusions:

  • The proposed dyadic shared-parameter model offers a robust approach for analyzing complex longitudinal dyadic data.
  • The method is applicable to studies with ordinal outcomes and informative missing data, as demonstrated in a breast cancer study.