Two-part hidden Markov models for semicontinuous longitudinal data with nonignorable missing covariates

Statistics in Medicine
Xiaoxiao ZhouXinyuan Song


This study develops a two-part hidden Markov model (HMM) for analyzing semicontinuous longitudinal data in the presence of missing covariates. The proposed model manages a semicontinuous variable by splitting it into two random variables: a binary indicator for determining the occurrence of excess zeros at all occasions and a continuous random variable for examining its actual level. For the continuous longitudinal response, an HMM is proposed to describe the relationship between the observation and unobservable finite-state transition processes. The HMM consists of two major components. The first component is a transition model for investigating how potential covariates influence the probabilities of transitioning from one hidden state to another. The second component is a conditional regression model for examining the state-specific effects of covariates on the response. A shared random effect is introduced to each part of the model to accommodate possible unobservable heterogeneity among observation processes and the nonignorability of missing covariates. A Bayesian adaptive least absolute shrinkage and selection operator (lasso) procedure is developed to conduct simultaneous variable selection and estimation. The proposed m...Continue Reading


Mar 26, 1998·American Journal of Medical Genetics·H HouldenJ Hardy
Jul 11, 2001·Neurobiology of Aging·S De SantiJ Fowler
Jun 14, 2003·The British Journal of Mathematical and Statistical Psychology·Sik-Yum Lee, Xin-Yuan Song
Aug 2, 2003·Nature Reviews. Neuroscience·Jon S Simons, Hugo J Spiers
Aug 25, 2004·Journal of Internal Medicine·R C Petersen
Mar 23, 2005·American Journal of Epidemiology·Laura Jean PodewilsConstantine G Lyketsos
Jan 16, 2007·Neurobiology of Aging·Lisa MosconiMony J de Leon
Feb 28, 2008·Journal of Magnetic Resonance Imaging : JMRI·Clifford R JackMichael W Weiner
May 4, 2011·Statistics in Medicine·Stacia M DeSantis, Dipankar Bandyopadhyay
Mar 2, 2012·Biometrics·Ruixin GuoJoseph G Ibrahim
Feb 5, 2013·Alzheimer's & Dementia : the Journal of the Alzheimer's Association·Kejal KantarciUNKNOWN Alzheimer's Disease Neuroimaging Initiative
Jul 15, 2015·Statistical Methods in Medical Research·Valerie A SmithMatthew L Maciejewski
Nov 24, 2017·Statistical Methods in Medical Research·Dan LiUNKNOWN Alzheimer’s Disease Neuroimaging Initiative
Jan 13, 2018·Multivariate Behavioral Research·Jingheng CaiXinyuan Song
Jul 25, 2018·Statistical Methods in Medical Research·Liangliang ZhangUNKNOWN and for the Alzheimer’s Disease Neuroimaging Initiative

❮ Previous
Next ❯


❮ Previous
Next ❯

Related Concepts

Related Feeds

Alzheimer's Disease: Neuroimaging

Neuroimaging can help identify pathological hallmarks of Alzheimer's disease (AD). Here is the latest research on neuroimaging modalities, including magnetic resonance imaging and positron emission tomography, in AD.