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An R-Based Landscape Validation of a Competing Risk Model
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FLCRM: Functional linear cox regression model.

Dehan Kong1, Joseph G Ibrahim2, Eunjee Lee3

  • 1Department of Statistical Sciences, University of Toronto, Ontario, Canada.

Biometrics
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Summary
This summary is machine-generated.

This study introduces a functional linear Cox regression model to predict time-to-event data using functional and scalar predictors. High-dimensional hippocampus surface data shows promise for predicting Alzheimer's disease conversion.

Keywords:
Cox regressionFunctional predictorFunctional principal component analysisScore test

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Neuroimaging Analysis

Background:

  • Time-to-event data analysis is crucial in medical research.
  • Predicting disease progression requires sophisticated modeling of complex data.
  • Functional and scalar predictors offer rich information for prognostic models.

Purpose of the Study:

  • To develop a functional linear Cox regression model for time-to-event data.
  • To incorporate functional principal component analysis and high-dimensional Cox regression.
  • To estimate parameters and test hypotheses regarding functional predictors in survival analysis.

Main Methods:

  • Functional linear Cox regression model.
  • Functional principal component analysis (FPCA) for functional predictors.
  • High-dimensional Cox regression for joint effects.
  • Algorithm for maximum approximate partial likelihood estimation.
  • Score test for nullity of slope function.

Main Results:

  • Developed and validated an estimation and testing procedure.
  • Demonstrated effectiveness through simulations.
  • Applied the model to Alzheimer's Disease Neuroimaging Initiative (ADNI) data.
  • Identified high-dimensional hippocampus surface data as a potential predictor for Alzheimer's disease conversion.

Conclusions:

  • The functional linear Cox regression model effectively characterizes associations between time-to-event data and functional/scalar predictors.
  • High-dimensional hippocampus surface data may serve as a significant biomarker for predicting Alzheimer's disease onset.
  • The developed methods provide a robust framework for analyzing complex biomedical data.