UPPGRAID: Understanding and Predicting Personalized Growth Rate curves to Aid
Individual prognosis in late-onset Dementia 
   Lead Investigator:    Miriam Haaksma
   Institution      :    Radboudumc   Johns Hopkins
   E-Mail           :    miriam.haaksma@radboudumc.nl
   Proposal ID      :    666


   Publications:


1  Haaksma M, Calder^{unicode 00F3}n-Larra^{unicode 00F1}aga A, Rikkert MGMO, Melis RJF, Leoutsakos JMS
   Latent classes of course in Alzheimer's Disease show an optimistic prognosis for the majority of patients
   2017   Abstract  International College of Geriatric Psychoneuropharmacology , 

   ACCEPTED



2  Haaksma ML, Calder^{unicode 00F3}n-Larra^{unicode 00F1}aga A, Rikkert MGMO, Melis RJF, Leoutsakos JS
   Cognitive and functional progression in Alzheimer disease: A prediction model of latent classes
   Journal Article  Int J Geriatr Psychiatry,8:1057-1064,2018,PMC6039270

   PUBLISHED



3  Wang Y, Haaksma ML, Ramakers I, Verhey F, van de Flier W, Rikkert M, Melis R
   Latent Classes of Dementia Progression Across Two Different Cohorts
   2019   Journal Article  Int J Geriatr Psychiatry , 

   SUBMITTED





   Proposal Description:
   This study aims to identify meaningful trajectories of dementia progression (as a multidimensional concept, taking into account both cognitive, functional and psychiatric decline), using latent class analysis and growth mixture modeling. This approach enables us to gain to more knowlegde on why the disease progresses quickly in some patients and slowly in others. In short, we would like to use the NACC dataset to externally validate multiple prognostic models in order to improve individualized prognosis.