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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Expectation maximization-based likelihood inference for flexible cure rate models with Weibull lifetimes.

Narayanaswamy Balakrishnan1, Suvra Pal2

  • 1Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada Department of Statistics, King Abdulaziz University, Jeddah, Saudi Arabia bala@univmail.cis.mcmaster.ca.

Statistical Methods in Medical Research
|June 7, 2013
PubMed
Summary

This study introduces a flexible cure rate survival model for competing risks, using the Weibull distribution and expectation maximization algorithm for cancer recurrence data analysis.

Keywords:
Akaike’s information criterionBayesian information criterionConway–Maxwell–Poisson distributionWeibull distributionasymptotic variancescure rate modelsexpectation maximization algorithmlifetime datalong-term survivormaximum likelihood estimatorsprofile likelihood

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Cancer Research

Background:

  • Cancer clinical trials often involve right-censored data.
  • Existing cure rate models may not fully capture competing risks.
  • Flexible survival models are needed for complex event data.

Purpose of the Study:

  • To develop and validate a flexible cure rate survival model for competing risks.
  • To adapt the expectation maximization algorithm for parameter estimation in Weibull-based cure rate models.
  • To analyze cancer recurrence data using the proposed methodology.

Main Methods:

  • Developed a cure rate survival model assuming competing causes follow a Weibull distribution.
  • Derived expectation maximization (EM) algorithm steps for parameter estimation.
  • Utilized the observed information matrix for standard error estimation.
  • Conducted extensive Monte Carlo simulations to assess performance.

Main Results:

  • The proposed expectation maximization algorithm efficiently estimates parameters for cure rate survival models with competing risks.
  • The methodology demonstrates robustness in simulation studies.
  • The model successfully analyzes real-world cancer recurrence data.

Conclusions:

  • The flexible cure rate survival model with competing risks and Weibull distribution is a valuable tool for cancer research.
  • The expectation maximization algorithm provides a reliable method for parameter estimation with right-censored data.
  • This approach enhances the analysis of complex survival data in clinical trials.