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Robust analysis in joint models: an application to a study on muscular dystrophy.

Kalyan Das1, Arindom Chakraborty

  • 1Department of Statistics, University of Calcutta, 35, B.C. Road, Kolkata-700 019, India. kalyanstat@gmail.com

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This study introduces a robust estimation method for joint generalized partial ordinal and time-to-event models, crucial for analyzing complex biomedical data and mitigating outlier sensitivity in longitudinal studies.

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Survival Analysis

Background:

  • Biomedical studies often involve ordinal longitudinal outcomes and time-dependent covariates.
  • Understanding the association between these outcomes and time-to-event data is critical for medical practitioners.
  • Existing joint models can be sensitive to outliers, impacting parameter estimation accuracy.

Purpose of the Study:

  • To investigate the robustness of estimators in joint generalized partial ordinal and time-to-event models.
  • To address the sensitivity of current estimators to outliers in biomedical data.
  • To propose a novel method for robust estimation in these complex models.

Main Methods:

  • Development and proposal of a Monte Carlo Metropolis-Hastings Newton Raphson algorithm for robust estimation.
  • Application of the proposed algorithm to joint generalized partial ordinal and time-to-event models.
  • Conducting a detailed simulation study to evaluate the performance of the new estimators.

Main Results:

  • The proposed algorithm demonstrates robust estimation properties, effectively handling outliers.
  • Simulation results confirm the reliable behavior of the developed robust estimators.
  • Analysis of a muscular dystrophy dataset revealed significant findings relevant to medical practice.

Conclusions:

  • The proposed robust estimation method enhances the reliability of parameter estimates in joint ordinal and time-to-event models.
  • This approach offers a valuable tool for analyzing complex biomedical data, particularly in the presence of outliers.
  • The findings provide practical insights for medical practitioners dealing with longitudinal outcomes and survival data.