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

作者：Benoit Julia S; Chan Wenyaw^*; Luo Sheng; Yeh Hung Wen; Doody Rachelle

来源：Statistics in Medicine, 2016, 35(9): 1549-1557.

DOI：10.1002/sim.6861

• longitudinal data analysis
• hidden Markov model
• disease progression
• misclassification

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.

• 出版日期2016-4-30