Hidden mover‐stayer model for disease progression accounting for misclassified and partially observed diagnostic tests: Application to the natural history of human papillomavirus and cervical precancer

• Published in: Statistics in medicine Vol. 40; no. 15; pp. 3460 - 3476
• Main Authors: Aron, Jordan, Albert, Paul S., Wentzensen, Nicolas, Cheung, Li C.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 10.07.2021
• Subjects: Algorithms; Alphapapillomavirus; Cervical cancer; Disease Progression; Early Detection of Cancer; EM algorithm; Female; forward‐backward algorithm; Human papillomavirus; Humans; measurement error; Medical screening; Missing data; Papillomaviridae; Papillomavirus Infections; partial missing data; Uterine Cervical Dysplasia; Uterine Cervical Neoplasms; measurement error; forward-backward algorithm; partial missing data; EM algorithm

Hidden Markov models (HMMs) have been proposed to model the natural history of diseases while accounting for misclassification in state identification. We introduce a discrete time HMM for human papillomavirus (HPV) and cervical precancer/cancer where the hidden and observed state spaces are defined...