Statistical Models of Tumour Onset and Growth for Modern Breast Cancer Screening Cohorts.

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. PMID: 31627176 [PubMed - as supplied by publisher]
Source: Mathematical Biosciences - Category: Statistics Authors: Tags: Math Biosci Source Type: research