A hidden Markov model approach to analyze longitudinal ternary outcomes when some observed states are possibly misclassified

Statistics in Medicine
Julia S BenoitRachelle Doody

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.

References

Nov 1, 1975·Journal of Psychiatric Research·M F FolsteinP R McHugh
Dec 28, 1999·Statistics in Medicine·Y Le Strat, F Carrat
Jan 17, 2003·Statistics in Medicine·Alexandre BureauJames P Hughes
Oct 16, 2003·Dementia and Geriatric Cognitive Disorders·Rachelle Smith DoodyMaria Kataki
May 24, 2005·Statistics in Medicine·Rachel MacKay Altman, A John Petkau
Aug 10, 2005·Dementia and Geriatric Cognitive Disorders·R DoodyWenyaw Chan
May 20, 2006·Biometrical Journal. Biometrische Zeitschrift·Yen-Peng Li, Wenyaw Chan
Jul 12, 2008·Statistics in Medicine·Ardo van den Hout, Fiona E Matthews

❮ Previous
Next ❯

Citations

Jun 24, 2020·American Journal of Alzheimer's Disease and Other Dementias·Julia S BenoitRachelle Doody
Jul 12, 2017·Statistics in Medicine·Maria Laura RubinClaudia Sue Robertson

❮ Previous
Next ❯

Related Concepts

Related Feeds

Alzheimer's Disease: Early Markers

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive and behavioral decline. Targeting markers in the earliest stages of the disease may mitigate the progression of AD. This feed focuses on early diagnosis and markers, as well as environmental, pharmacological, and drug-response biomarkers associated with this disease.