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Sampling-based Markov regression model for multistate disease progression: Applications to population-based cancer

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Summary

This study introduces a new sampling-based Markov regression model for accurate disease natural history analysis. It enables personalized screening strategies by efficiently using follow-up data, reducing costs associated with population-wide screening.

Keywords:
Markov exponential regression modelTwo-stage sampling designmultistate model

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Developing personalized screening requires accurate disease natural history models.
  • Population-wide screening data is costly and often infeasible for new biomarkers.
  • Existing case-cohort designs may suffer from length-bias and ignore test sensitivity.

Purpose of the Study:

  • To propose a novel sampling-based Markov regression model for multistate disease natural history.
  • To accommodate follow-up data from various detection modes for accurate analysis.
  • To develop simulation algorithms for sample size and sampling fraction determination.

Main Methods:

  • Adaptation of a non-standard case-cohort design with two-stage sampling.
  • Development of sampling-based Markov regression models and variants.
  • Computer simulations for sample size and sampling fraction optimization.

Main Results:

  • The proposed model accurately constructs state-specific covariate-based multistate disease natural history.
  • Simulation algorithms were developed for sample size and sampling fraction determination.
  • Application to breast and colorectal cancer screening data demonstrated model utility.

Conclusions:

  • The novel model offers an accurate and efficient approach to disease natural history modeling.
  • It facilitates personalized screening and surveillance strategies by leveraging follow-up data.
  • The developed methods aid in optimizing screening program design and resource allocation.