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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Vydané v:	Statistics in medicine Ročník 40; číslo 15; s. 3460 - 3476
Hlavní autori:	Aron, Jordan, Albert, Paul S., Wentzensen, Nicolas, Cheung, Li C.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Wiley Subscription Services, Inc 10.07.2021
Predmet:	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
On-line prístup:	Získať plný text
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