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Related Experiment Video

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Multivariate piecewise joint models with random change-points for skewed-longitudinal and survival data.

Yangxin Huang1,2, Nian-Sheng Tang2, Jiaqing Chen3

  • 1College of Public Health, University of South Florida, Tampa, FL, USA.

Journal of Applied Statistics
|August 29, 2022
PubMed
Summary

This study introduces advanced Bayesian joint models for analyzing complex health data. These models better capture longitudinal patient responses and their association with event times, improving disease diagnosis and treatment assessment.

Keywords:
Bayesian inferencemultivariate longitudinal-survival datamultivariate piecewise joint modelsrandom change-pointsskew-t distribution

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Survival Analysis

Background:

  • Traditional joint models for longitudinal and time-to-event data often use simplified linear mixed-effects and Cox models.
  • These standard approaches may not adequately represent complex biological processes, such as nonlinear longitudinal trajectories or non-normal data distributions.
  • Ignoring correlations among multiple longitudinal outcomes can lead to biased results in survival analysis.

Purpose of the Study:

  • To develop and apply Bayesian multivariate piecewise joint models for correlated longitudinal data.
  • To address departures from normality in longitudinal measures using skewed distributions.
  • To incorporate random change-points to better model nonlinear longitudinal trajectories and their association with event times.

Main Methods:

  • Proposed Bayesian multivariate piecewise joint models incorporating skewed distributions and random change-points.
  • Utilized a piecewise (broken-stick) nonlinear approach to model longitudinal trajectories.
  • Linked multivariate longitudinal outcomes to time-to-event processes within a Bayesian framework.

Main Results:

  • Demonstrated the methodology using a real-world motivating example.
  • Simulation studies confirmed the performance and accuracy of the proposed joint models.
  • The models effectively handle correlated multivariate longitudinal data and non-normal distributions.

Conclusions:

  • The proposed Bayesian multivariate piecewise joint models offer a flexible and robust approach for analyzing complex longitudinal and survival data.
  • These models provide improved accuracy in quantifying treatment effects and disease progression by accounting for nonlinear trajectories and data complexities.
  • The methodology enhances clinical decision-making by providing more reliable estimates of the association between longitudinal health measures and event times.