A hidden Markov model addressing measurement errors in the response and replicated covariates for continuous nondecreasing processes. (23rd February 2019)

• Record Type: Journal Article
• Authors: Naranjo, Lizbeth; Pérez, Carlos J; Fuentes-García, Ruth; Martín, Jacinto
• Is Part Of: Biostatistics. Volume 21:Number 4(2020)
• Page Start: 743
• Page End: 757
• Publication Date: 2019-02-23
• Subjects: Bayesian methods -- Measurement error -- Nondecreasing process -- Parkinson's disease -- Replicated measurements -- Voice features; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxz004
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: Motivated by a study tracking the progression of Parkinson's disease (PD) based on features extracted from voice recordings, an inhomogeneous hidden Markov model with continuous state-space is proposed. The approach addresses the measurement error in the response, the within-subject variability of the replicated covariates and presumed nondecreasing response. A Bayesian framework is described and an efficient Markov chain Monte Carlo method is developed. The model performance is evaluated through a simulation-based example and the analysis of a PD tracking progression dataset is presented. Although the approach was motivated by a PD tracking progression problem, it can be applied to any monotonic nondecreasing process whose continuous response variable is subject to measurement errors and where replicated covariates play a key role.