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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Vydáno v:	Statistics in medicine Ročník 40; číslo 15; s. 3460 - 3476
Hlavní autoři:	Aron, Jordan, Albert, Paul S., Wentzensen, Nicolas, Cheung, Li C.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Wiley Subscription Services, Inc 10.07.2021
Témata:	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
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Abstract	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 by all possible combinations of HPV, cytology, and colposcopy results. Because the population of women undergoing cervical cancer screening is heterogeneous with respect to sexual behavior, and therefore risk of HPV acquisition and subsequent precancers, we use a mover‐stayer mixture model that assumes a proportion of the population will stay in the healthy state and are not subject to disease progression. As each state is a combination of three distinct tests that characterize the cervix, partially observed data arise when at least one but not every test is observed. The standard forward‐backward algorithm, used for evaluating the E‐step within the E‐M algorithm for maximum‐likelihood estimation of HMMs, cannot incorporate time points with partially observed data. We propose a new forward‐backward algorithm that considers all possible fully observed states that could have occurred across a participant's follow‐up visits. We apply our method to data from a large management trial for women with low‐grade cervical abnormalities. Our simulation study found that our method has relatively little bias and out preforms simpler methods that resulted in larger bias.
AbstractList	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 by all possible combinations of HPV, cytology, and colposcopy results. Because the population of women undergoing cervical cancer screening is heterogeneous with respect to sexual behavior, and therefore risk of HPV acquisition and subsequent precancers, we use a mover-stayer mixture model that assumes a proportion of the population will stay in the healthy state and are not subject to disease progression. As each state is a combination of three distinct tests that characterize the cervix, partially observed data arise when at least one but not every test is observed. The standard forward-backward algorithm, used for evaluating the E-step within the E-M algorithm for maximum-likelihood estimation of HMMs, cannot incorporate time points with partially observed data. We propose a new forward-backward algorithm that considers all possible fully observed states that could have occurred across a participant’s follow-up visits. We apply our method to data from a large management trial for women with low-grade cervical abnormalities. Our simulation study found that our method has relatively little bias and out preforms simpler methods that resulted in larger bias. 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 by all possible combinations of HPV, cytology, and colposcopy results. Because the population of women undergoing cervical cancer screening is heterogeneous with respect to sexual behavior, and therefore risk of HPV acquisition and subsequent precancers, we use a mover-stayer mixture model that assumes a proportion of the population will stay in the healthy state and are not subject to disease progression. As each state is a combination of three distinct tests that characterize the cervix, partially observed data arise when at least one but not every test is observed. The standard forward-backward algorithm, used for evaluating the E-step within the E-M algorithm for maximum-likelihood estimation of HMMs, cannot incorporate time points with partially observed data. We propose a new forward-backward algorithm that considers all possible fully observed states that could have occurred across a participant's follow-up visits. We apply our method to data from a large management trial for women with low-grade cervical abnormalities. Our simulation study found that our method has relatively little bias and out preforms simpler methods that resulted in larger bias.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 by all possible combinations of HPV, cytology, and colposcopy results. Because the population of women undergoing cervical cancer screening is heterogeneous with respect to sexual behavior, and therefore risk of HPV acquisition and subsequent precancers, we use a mover-stayer mixture model that assumes a proportion of the population will stay in the healthy state and are not subject to disease progression. As each state is a combination of three distinct tests that characterize the cervix, partially observed data arise when at least one but not every test is observed. The standard forward-backward algorithm, used for evaluating the E-step within the E-M algorithm for maximum-likelihood estimation of HMMs, cannot incorporate time points with partially observed data. We propose a new forward-backward algorithm that considers all possible fully observed states that could have occurred across a participant's follow-up visits. We apply our method to data from a large management trial for women with low-grade cervical abnormalities. Our simulation study found that our method has relatively little bias and out preforms simpler methods that resulted in larger bias.
Author	Cheung, Li C. Wentzensen, Nicolas Aron, Jordan Albert, Paul S.
AuthorAffiliation	1 Biostatistics Branch, Division of Cancer and Epidemiology, National Cancer Institute, Rockville, Maryland, USA 2 Clinical Genetics Branch, Division of Cancer and Epidemiology, National Cancer Institute, Rockville, Maryland, USA
AuthorAffiliation_xml	– name: 1 Biostatistics Branch, Division of Cancer and Epidemiology, National Cancer Institute, Rockville, Maryland, USA – name: 2 Clinical Genetics Branch, Division of Cancer and Epidemiology, National Cancer Institute, Rockville, Maryland, USA
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SubjectTerms	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
Title	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
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