Likelihood function for estimating parameters in multistate disease process with Laplace-transformation-based transition probabilities

Ting Yu Lin, Ming-Fang Yen, Tony Hsiu Hsi Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Multistate statistical models are often used to characterize the complex multi-compartment progression of the disease such as cancer. However, the derivation of multistate transition kernels is often involved with the intractable convolution that requires intensive computation. Moreover, the estimation of parameters pertaining to transition kernel requires the individualized time-stamped history data while the traditional likelihood function forms are constructed. In this paper, we came up with a novel likelihood function derived from Laplace transformation-based transition probabilities in conjunction with Expectation–Maximization algorithm to estimate parameters. The proposed method was applied to two large population-based screening data with only aggregated count data without relying on individual time-stamped history data.

Original languageEnglish
Article number108586
JournalMathematical Biosciences
Volume335
DOIs
Publication statusPublished - May 2021

Keywords

  • Laplace transformation
  • Multi-state Markov model
  • Sufficient statistics

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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