title: A hidden Markov model approach to analyze longitudinal ternary outcomes when some observed states are possibly misclassified.
abstract: Understanding the dynamic disease process is vital in early detection, diagnosis, and measuring progression. Continuous-time Markov chain (CTMC) methods have been used to estimate state-change intensities but challenges arise when stages are potentially misclassified. We present an analytical likelihood approach where the hidden state is modeled as a three-state CTMC model allowing for some observed states to be possibly misclassified. Covariate effects of the hidden process and misclassification probabilities of the hidden state are estimated without information from a 'gold standard' as comparison. Parameter estimates are obtained using a modified expectation-maximization (EM) algorithm, and identifiability of CTMC estimation is addressed. Simulation studies and an application studying Alzheimer's disease caregiver stress-levels are presented. The method was highly sensitive to detecting true misclassification and did not falsely identify error in the absence of misclassification. In conclusion, we have developed a robust longitudinal method for analyzing categorical outcome data when classification of disease severity stage is uncertain and the purpose is to study the process' transition behavior without a gold standard.
authors.affiliation: Texas Institute for Measurement, Evaluation, and Statistics and Department of Basic Vision Sciences, College of Optometry, The University of Houston, Houston, TX, 77204, U.S.A.|Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, U.S.A.
authors.forename: Julia S
authors.initials: JS
authors.lastname: Benoit
authors.affiliation: Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, U.S.A.
authors.forename: Wenyaw
authors.initials: W
authors.lastname: Chan
authors.affiliation: Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, U.S.A.
authors.forename: Sheng
authors.identifier: http://orcid.org/0000-0003-4214-5809
authors.initials: S
authors.lastname: Luo
authors.affiliation: Department of Biostatistics, The University of Kansas Medical Center, Kansas City, KS, 66160, U.S.A.
authors.forename: Hung-Wen
authors.initials: HW
authors.lastname: Yeh
authors.affiliation: Alzheimer's Disease and Memory Disorders Center, Department of Neurology, Baylor College of Medicine, Houston, TX, 77030, U.S.A.
authors.forename: Rachelle
authors.initials: R
authors.lastname: Doody
mesh_terms: D000465:Algorithms; D000068598:Data Accuracy; D003627:Data Interpretation, Statistical; D042241:Early Diagnosis; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D015233:Models, Statistical; D013997:Time Factors; D016896:Treatment Outcome
keywords: disease progression; hidden Markov model; longitudinal data analysis; misclassification