Approximate Bayesian computation for the natural history of breast cancer, with application to data from a Milan cohort study

Summary We explore models for the natural history of breast cancer, where the main events of interest are the start of asymptomatic detectability of the disease (through screening) and the time of symptomatic detection (through symptoms). We develop several parametric specifications based on a cure...

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Bibliographic Details
Published in:	Statistics in medicine Vol. 42; no. 18; pp. 3093 - 3113
Main Authors:	Bondi, Laura, Bonetti, Marco, Grigorova, Denitsa, Russo, Antonio
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 15.08.2023 Wiley Subscription Services, Inc
Subjects:	Algorithms approximate Bayesian computation Asymptomatic Bayes Theorem Breast cancer Breast Neoplasms - epidemiology Cohort analysis Cohort Studies Computer Simulation Female Humans Italy - epidemiology Likelihood Functions Medical screening Missing data multi‐state modeling natural history Parameter estimation screening Italy breast cancer screening natural history approximate Bayesian computation missing data multi-state modeling
ISSN:	0277-6715, 1097-0258, 1097-0258
Online Access:	Get full text
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