title: Estimation and validation of a multiattribute model of Alzheimer disease progression.
abstract: OBJECTIVE
  To estimate and validate a multiattribute model of the clinical course of Alzheimer disease (AD) from mild AD to death in a high-quality prospective cohort study, and to estimate the impact of hypothetical modifications to AD progression rates on costs associated with Medicare and Medicaid services.
  
  
  METHODS
  The authors estimated sex-specific longitudinal Grade of Membership (GoM) models for AD patients (103 men, 149 women) in the initial cohort of the Predictors Study (1989-2001) based on 80 individual measures obtained every 6 mo for 10 y. These models were replicated for AD patients (106 men, 148 women) in the 2nd Predictors Study cohort (1997-2007). Model validation required that the disease-specific transition parameters be identical for both Predictors Study cohorts. Medicare costs were estimated from the National Long Term Care Survey.
  
  
  RESULTS
  Sex-specific models were validated using the 2nd Predictors Study cohort with the GoM transition parameters constrained to the values estimated for the 1st Predictors Study cohort; 57 to 61 of the 80 individual measures contributed significantly to the GoM models. Simulated, cost-free interventions in the rate of progression of AD indicated that large potential cost offsets could occur for patients at the earliest stages of AD.
  
  
  CONCLUSIONS
  AD progression is characterized by a small number of parameters governing changes in large numbers of correlated indicators of AD severity. The analysis confirmed that the progression of AD represents a complex multidimensional physiological process that is similar across different study cohorts. The estimates suggested that there could be large cost offsets to Medicare and Medicaid from the slowing of AD progression among patients with mild AD. The methodology appears generally applicable in AD modeling.
authors.affiliation: Department of Sociology and Center for Population Health and Aging, Duke University, Durham, North Carolina, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Stallard
authors.forename: Bruce
authors.initials: B
authors.lastname: Kinosian
authors.forename: Arthur S
authors.initials: AS
authors.lastname: Zbrozek
authors.forename: Anatoliy I
authors.initials: AI
authors.lastname: Yashin
authors.forename: Henry A
authors.initials: HA
authors.lastname: Glick
authors.forename: Yaakov
authors.initials: Y
authors.lastname: Stern
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000191:economics / Q000473:pathology*; D001499:Bayes Theorem; D015415:Biomarkers; D003657:Decision Making; D003661:Decision Support Techniques; D018450:Disease Progression; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008297:Male; D008390:Markov Chains; D008484:Medicaid; D006278:Medicare; D018803:Models, Economic; D015233:Models, Statistical; D016016:Proportional Hazards Models; D011594:Psychometrics; D012737:Sex Factors; D014481:United States
chemical_list: D015415:Biomarkers