Non-homogeneous Markov process models for rate of conversion to MCI 

 
   Lead Investigator:    Rebecca Hubbard
   Institution      :    NACC
   E-Mail           :    azhou@u.washington.edu
   Proposal ID      :    43


   Publications:


1  Chen B, Zhou A
   A Correlated Random Effects Model for Non-homogeneous Markov Process with Nonignorable Missingness
   Journal Article  J Multivar Anal,117:41287,2013,PMC3697104

   PUBLISHED



2  Chen B, Zhou XH
   Non-homogeneous Markov process models with informative observations with an application to Alzheimer's disease
   Journal Article  Biom J,53:444-463,2011,PMC3631600

   PUBLISHED



3  Hubbard R, Zhou XH
   A non-homogeneous Markov process model for Alzheimer's disease progression
   2008   Abstract  Int Biometrics Society   159   , 

   PUBLISHED



4  Hubbard R
   Identifying risk factors for conversion between normal cognition, MCI, and dementia using Markov processes
   Abstract  Alzheimers Dement,4:T675-676,2008,

   PUBLISHED



5  Hubbard RA, Zhou A
   Modeling Risk Factors for Alzheimer's Disease Progression Using a Nonhomogeneous Markov Process
   2008   Abstract  Joint Statistical Meeting   193   , 

   PUBLISHED



6  Hubbard RA, Zhou XH
   A comparison of non-homogeneous Markov regression models with application to Alzheimer's disease progression
   Journal Article  J Appl Stat,38:2313-2326,2011,PMC3299197

   PUBLISHED



7  Koepsell T, Monsell S
   Testing the First-Order Markov Assumption in Homogeneous Markov Modeling of Transition Probabilities between Cognitive States
   Abstract  Alzheimers Dement,7:S351-352,2011,

   PUBLISHED





   Proposal Description:
   Aim 1: Develop and apply statistical methods for non-homogeneous multi-state  disease to transitions between cognitive states in order to estimate conversion  rates from normal cognition to MCI and from MCI to AD        Aim 2: Use covariate dependence of transition rates to identify risk factors  for increased rate of conversion