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

• 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
• DOI: 10.1002/sim.9756

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...