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Statistical models of tumour onset and growth for modern breast cancer screening cohorts.

Authors
  • Strandberg, J Rickard1
  • Humphreys, Keith2
  • 1 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Solna SE-171 77, Sweden. Electronic address: [email protected] , (Sweden)
  • 2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Solna SE-171 77, Sweden. , (Sweden)
Type
Published Article
Journal
Mathematical biosciences
Publication Date
Dec 01, 2019
Volume
318
Pages
108270–108270
Identifiers
DOI: 10.1016/j.mbs.2019.108270
PMID: 31627176
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Historically, multi-state Markov models have been used to study breast cancer incidence and mammography screening effectiveness. In recent years, more biologically motivated continuous tumour growth models have emerged as alternatives. However, a number of challenges remain for these models to make use of the wealth of information available in large mammography cohort data. In particular, methodology is needed to address random left truncation and individual, asynchronous screening. We present a comprehensive continuous random effects model for the natural history of breast cancer. It models the unobservable processes of tumour onset, tumour growth, screening sensitivity, and symptomatic detection. We show how the unknown model parameter values can be jointly estimated using a prospective cohort with diagnostic data on age and tumour size at diagnosis, and individual screening histories. We also present a microsimulation study calibrated to population breast cancer incidence data, and to data on mode of detection and tumour size. We highlight the importance of adjusting for random left truncation, derive tumour doubling time distributions for screen-detected and interval cancers, and present results concerning the relationship between tumour presence time and age at diagnosis. Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.

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