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

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...

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Bibliographic Details
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
ISSN:	0277-6715, 1097-0258, 1097-0258
Online Access:	Get full text
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