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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Flexible and Interpretable Models for Survival Data.

Jiacheng Wu1, Daniela Witten1,2

  • 1Department of Biostatistics, University of Washington, Seattle, WA.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|July 25, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an additive Cox proportional hazards model using trend filtering for flexible and interpretable survival data analysis. The method efficiently models covariate effects, showing promise in medical and clinical trial datasets.

Keywords:
Additive modelCox’s modelPiece-wise polynomialTrend filtering

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Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Increasing dataset sizes necessitate flexible and interpretable prediction methods.
  • Recent work focuses on additive modeling in regression with data-adaptive nonlinear functions.
  • Extending these flexible modeling approaches to survival analysis is of significant interest.

Purpose of the Study:

  • To develop an additive Cox proportional hazards model for survival data.
  • To incorporate trend filtering for data-adaptive nonlinear function estimation.
  • To provide an efficient algorithm for model fitting and demonstrate its utility.

Main Methods:

  • An additive Cox proportional hazards model is proposed.
  • Trend filtering is employed to estimate piece-wise polynomial functions with adaptive knots.
  • A proximal gradient descent algorithm is utilized for efficient model fitting.

Main Results:

  • The proposed model offers a flexible and interpretable approach to survival data analysis.
  • Trend filtering effectively captures nonlinear covariate effects.
  • The proximal gradient descent algorithm provides efficient computation.

Conclusions:

  • The developed additive Cox model with trend filtering is a valuable tool for survival analysis.
  • The method is demonstrated to be effective on real-world datasets, including primary biliary cirrhosis and clinical trial data.
  • This approach enhances interpretability and flexibility in modeling survival outcomes.