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Partly linear single-index cure models with a nonparametric incidence link function.

Chun Yin Lee1, Kin Yau Wong1,2, Dipankar Bandyopadhyay3

  • 1Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong.

Statistical Methods in Medical Research
|February 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces flexible semiparametric mixture cure models for cancer survival analysis. The new method improves modeling of covariate effects on cure and survival rates, enhancing patient outcome predictions.

Keywords:
Bernstein polynomialexpectation-maximization algorithmmixture cure modelssieve estimationsurvival analysis

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Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Cancer studies often involve cured patients, complicating survival analysis.
  • Standard mixture cure models have limitations in modeling covariate effects on cure and latency.
  • Covariates can influence both cancer recurrence (incidence) and time to event (latency).

Purpose of the Study:

  • To develop flexible semiparametric mixture cure models for cancer survival data.
  • To overcome limitations of traditional models regarding covariate effect structures.
  • To provide a more accurate framework for analyzing cancer patient outcomes.

Main Methods:

  • Proposed a class of semiparametric mixture cure models with single-index functions.
  • Employed a hybrid nonparametric maximum likelihood estimation (NPMLE) approach.
  • Utilized Bernstein polynomials for estimating regression components and an expectation-maximization algorithm for parameter estimation.

Main Results:

  • The proposed method offers enhanced flexibility in modeling covariate effects.
  • Simulation studies demonstrated the good finite-sample performance of the estimator.
  • The methodology was successfully applied to real-world cancer datasets.

Conclusions:

  • The developed semiparametric mixture cure models provide a flexible and effective tool for cancer survival analysis.
  • The proposed estimation method is robust and performs well in practice.
  • This approach can lead to improved understanding and prediction of cancer patient outcomes.