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Related Experiment Video

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Bayesian quantile regression-based nonlinear mixed-effects joint models for time-to-event and longitudinal data with

Yangxin Huang1, Jiaqing Chen2

  • 1Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, FL 33612, U.S.A.

Statistics in Medicine
|September 6, 2016
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Summary
This summary is machine-generated.

This study introduces Bayesian joint models to analyze complex longitudinal data, including measurement errors and missing values. The approach provides robust statistical inference for quantile regression, covariates, and time-to-event outcomes.

Keywords:
Dirichlet processQR-based joint modelsasymmetric Laplace distributionlongitudinal and event time datameasurement error modelsskewed distributions

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Longitudinal Data Analysis

Background:

  • Longitudinal data often exhibit complex features like non-linearity, non-normality, measurement error, and missing values.
  • Simultaneous analysis of these features is crucial for reliable statistical inference.
  • Existing methods may struggle to account for all these data characteristics concurrently.

Purpose of the Study:

  • To develop a Bayesian joint modeling approach for analyzing longitudinal data with a quantile of response, a mismeasured covariate, and an event time outcome.
  • To characterize the entire conditional distribution of the response variable using quantile regression.
  • To address measurement error, non-ignorable missing observations, and departures from normality in covariates, and interval-censored event times.

Main Methods:

  • Developed a Bayesian joint modeling framework integrating three models: a quantile regression-based nonlinear mixed-effects model for the response (using asymmetric Laplace distribution), a linear mixed-effects model for the mismeasured covariate (with skew-t distribution and accounting for informative missingness), and an accelerated failure time model for event time (with unspecified nonparametric distribution).
  • Applied the proposed approach to an AIDS clinical data set.
  • Conducted simulation studies to evaluate the performance of the joint models.

Main Results:

  • The Bayesian joint modeling approach effectively handles complex data features, including quantile response, mismeasured covariates with missingness, and interval-censored event times.
  • The models provide robust and reliable inferential results by simultaneously accounting for non-central location, non-linearity, non-normality, measurement error, and missing values.
  • Demonstrated the utility of the approach through application to a real-world AIDS clinical data set.

Conclusions:

  • The proposed Bayesian joint modeling approach offers a robust framework for analyzing complex longitudinal and survival data.
  • This method enhances statistical inference accuracy by simultaneously addressing multiple data challenges.
  • The approach is valuable for applications in biostatistics and other fields dealing with intricate data structures.