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Promotion time cure rate model with a neural network estimated nonparametric component.

Yujing Xie1, Zhangsheng Yu1,2

  • 1School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.

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|April 30, 2021
PubMed
Summary

This study introduces a novel neural network approach for promotion time cure rate models (PCM) to effectively incorporate complex medical image data. The method enhances prediction and estimation accuracy for survival data with a cure fraction.

Keywords:
EM algorithmconvergence ratecure rate modelsmachine learningsurvival analysis

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Area of Science:

  • Biostatistics
  • Medical Imaging Analysis
  • Machine Learning in Survival Analysis

Background:

  • Promotion time cure rate models (PCM) are vital for analyzing survival data with a cure fraction.
  • Integrating complex predictors like medical images into traditional PCMs presents significant challenges.
  • Nonparametric methods like splines struggle with unstructured predictor effects from medical imaging.

Purpose of the Study:

  • To develop a novel method for incorporating medical image data into promotion time cure rate models.
  • To leverage neural networks for modeling unstructured predictors within the PCM framework.
  • To improve the accuracy of survival data analysis when cure fractions are present and image-based predictors are utilized.

Main Methods:

  • Proposed a novel approach using neural networks within the PCM framework to handle unstructured predictors.
  • Employed an expectation-maximization algorithm with neural networks for parameter estimation.
  • Derived the asymptotic properties of the proposed parameter estimates.

Main Results:

  • Simulation studies demonstrated the effectiveness of the proposed method in terms of both prediction and estimation.
  • The neural network approach successfully modeled the effects of complex image-derived predictors.
  • The method showed good performance in analyzing survival data with cure fractions.

Conclusions:

  • The proposed neural network-based PCM offers a powerful tool for survival data analysis incorporating medical imaging.
  • This method overcomes limitations of traditional approaches in handling unstructured predictors.
  • The approach was successfully applied to analyze brain imaging data, demonstrating its practical utility.