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Bounded influence function based inference in joint modelling of ordinal partial linear model and accelerated failure

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Summary

This study introduces a robust method for analyzing longitudinal data with time-to-event outcomes, addressing challenges like ordinal responses and missing data. The new approach improves analysis reliability by mitigating the impact of influential observations.

Keywords:
MCMHNR algorithmlongitudinalmixed effects modelmuscular dystrophy syndromeordinal data

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Survival Analysis

Background:

  • Longitudinal studies often involve complex data structures, including repeated ordered responses and time-to-event outcomes.
  • Analyzing such data jointly presents challenges due to ordinal response nature and potential missing covariate information.
  • Influential observations can significantly bias results in classical statistical modeling.

Purpose of the Study:

  • To develop a robust joint modeling framework for longitudinal ordinal responses and time-to-event data.
  • To propose an influence function-based robust estimation method to handle influential observations.
  • To evaluate the performance of the proposed method through simulation studies and a real-world application.

Main Methods:

  • A joint model combining an ordinal partial mixed model and an accelerated failure time model was employed.
  • A robust estimation method utilizing influence functions was developed.
  • Parameter estimation was performed using a Monte Carlo Expectation Maximization (EM) algorithm.

Main Results:

  • Simulation studies demonstrated the effectiveness of the proposed robust method in parameter estimation.
  • The robust estimates were compared to classical maximum likelihood estimates, showing improved performance.
  • Application to muscular dystrophy data illustrated the practical utility of the robust approach.

Conclusions:

  • The proposed influence function-based robust estimation method effectively addresses challenges in joint modeling of longitudinal ordinal data and time-to-event data.
  • This robust approach provides more reliable estimates in the presence of influential observations.
  • The method is applicable to various fields, including biomedical research, as shown by the muscular dystrophy study.