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Concrete members with a small surface-to-volume ratio are cured by oiling and moistening the forms before casting the concrete member. These forms can be left in place for a prolonged period to prevent moisture loss, and can be wetted if made of a material suitable for wetting. If the forms are removed early, the concrete member is moistened and covered with polythene sheets to maintain moisture. For large horizontal concrete surfaces exposed to dry weather, a temporary covering is suspended...
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EM algorithms for fitting multistate cure models.

Lauren J Beesley1, Jeremy M G Taylor1

  • 1School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, USA.

Biostatistics (Oxford, England)
|March 28, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new Expectation-Maximization (EM) algorithm for multistate cure models, simplifying cancer recurrence and survival analysis. The Monte Carlo EM (MCEM) algorithm improves accessibility for researchers studying cancer patient outcomes.

Keywords:
Cure modelsEM algorithmMonte Carlo EM algorithmMultistate models

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Area of Science:

  • Biostatistics
  • Cancer Research
  • Survival Analysis

Background:

  • Multistate cure models analyze populations with an unrecoverable fraction, crucial for cancer studies.
  • These models identify factors influencing cancer recurrence, post-recurrence mortality, and cure probability.
  • Existing fitting methods require extensive custom programming, limiting accessibility.

Purpose of the Study:

  • To present an accessible Expectation-Maximization (EM) algorithm for fitting multistate cure models.
  • To develop a Monte Carlo EM (MCEM) algorithm addressing missing covariates and unequal follow-up times.
  • To provide a novel method for standard error estimation and accompanying software.

Main Methods:

  • Developed a maximum likelihood estimation approach using a weighted likelihood representation.
  • Implemented an Expectation-Maximization (EM) algorithm for model fitting.
  • Proposed a Monte Carlo EM (MCEM) algorithm to handle missing covariate data and differential follow-up times.

Main Results:

  • The proposed EM algorithm is easily implementable with standard statistical software.
  • The MCEM algorithm effectively handles missing covariates and unequal follow-up periods.
  • Simulations show good performance under restrictive modeling assumptions.

Conclusions:

  • The new EM algorithm enhances the accessibility of multistate cure models for cancer research.
  • The MCEM approach provides a robust method for complex survival data scenarios.
  • The developed software and methods facilitate the analysis of cancer recurrence and survival.