title: Measuring Alzheimer's disease progression with transition probabilities: estimates from CERAD.
abstract: OBJECTIVE
  To estimate annual transition probabilities (i.e., the likelihood that a patient will move from one disease stage to another in a given time period) for AD progression. Transition probabilities are estimated by disease stages (mild, moderate, severe) and settings of care (community, nursing home), accounting for differences in age, gender, and behavioral symptoms as well as the length of time a patient has been in a disease stage.
  
  
  METHODS
  Using data from the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), the authors employed a modified survival analysis to estimate stage-to-stage and stage-to-nursing home transition probabilities. To account for individual variability, a Cox proportional hazards model was fit to the CERAD data to estimate hazard ratios for gender, age (50 to 64, 65 to 74, and more than 75 years), and level of behavioral symptoms (low/high, according to responses to the Behavioral Rating Scale for Dementia) for each of the key stage-to-stage and stage-to-nursing home transitions.
  
  
  RESULTS
  The transition probabilities underscore the rapid progression of patients into more severe disease stages and into nursing homes and the differences among population subgroups. In general, male gender, age under 65, and high level of behavioral symptoms were associated with higher transition probabilities to more severe disease stages. Disease progression is roughly constant as a function of the time a patient has spent in a particular stage.
  
  
  CONCLUSIONS
  Transition probabilities provide a useful means of characterizing AD progression. Economic models of interventions for AD should consider the varied course of progression for different population subgroups, particularly those defined by high levels of behavioral symptoms.
authors.affiliation: Program on the Economic Evaluation of Medical Technology, Center for Risk Analysis, Harvard School of Public Health, Boston, MA 02115, USA. pneumann@hsph.harvard.edu
authors.forename: P J
authors.initials: PJ
authors.lastname: Neumann
authors.forename: S S
authors.initials: SS
authors.lastname: Araki
authors.forename: A
authors.initials: A
authors.lastname: Arcelus
authors.forename: A
authors.initials: A
authors.lastname: Longo
authors.forename: G
authors.initials: G
authors.lastname: Papadopoulos
authors.forename: K S
authors.initials: KS
authors.lastname: Kosik
authors.forename: K M
authors.initials: KM
authors.lastname: Kuntz
authors.forename: A
authors.initials: A
authors.lastname: Bhattacharjya
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis* / Q000191:economics / Q000453:epidemiology; D018450:Disease Progression; D005260:Female; D006707:Homes for the Aged / Q000191:economics; D006801:Humans; D008297:Male; D018803:Models, Economic; D009735:Nursing Homes / Q000191:economics; D011336:Probability; D016016:Proportional Hazards Models; D012042:Registries / Q000706:statistics & numerical data*; D014481:United States / Q000453:epidemiology