title: Modeling screening, prevention, and delaying of Alzheimer's disease: an early-stage decision analytic model.
abstract: BACKGROUND
  Alzheimer's Disease (AD) affects a growing proportion of the population each year. Novel therapies on the horizon may slow the progress of AD symptoms and avoid cases altogether. Initiating treatment for the underlying pathology of AD would ideally be based on biomarker screening tools identifying pre-symptomatic individuals. Early-stage modeling provides estimates of potential outcomes and informs policy development.
  
  
  METHODS
  A time-to-event (TTE) simulation provided estimates of screening asymptomatic patients in the general population age > or =55 and treatment impact on the number of patients reaching AD. Patients were followed from AD screen until all-cause death. Baseline sensitivity and specificity were 0.87 and 0.78, with treatment on positive screen. Treatment slowed progression by 50%. Events were scheduled using literature-based age-dependent incidences of AD and death.
  
  
  RESULTS
  The base case results indicated increased AD free years (AD-FYs) through delays in onset and a reduction of 20 AD cases per 1000 screened individuals. Patients completely avoiding AD accounted for 61% of the incremental AD-FYs gained. Total years of treatment per 1000 screened patients was 2,611. The number-needed-to-screen was 51 and the number-needed-to-treat was 12 to avoid one case of AD. One-way sensitivity analysis indicated that duration of screening sensitivity and rescreen interval impact AD-FYs the most. A two-way sensitivity analysis found that for a test with an extended duration of sensitivity (15 years) the number of AD cases avoided was 6,000-7,000 cases for a test with higher sensitivity and specificity (0.90,0.90).
  
  
  CONCLUSIONS
  This study yielded valuable parameter range estimates at an early stage in the study of screening for AD. Analysis identified duration of screening sensitivity as a key variable that may be unavailable from clinical trials.
authors.affiliation: Medical Decision Modeling Inc., Indianapolis, IN, USA. nf@mdm-inc.com
authors.forename: Nicolas M
authors.initials: NM
authors.lastname: Furiak
authors.forename: Robert W
authors.initials: RW
authors.lastname: Klein
authors.forename: Kristin
authors.initials: K
authors.lastname: Kahle-Wrobleski
authors.forename: Eric R
authors.initials: ER
authors.lastname: Siemers
authors.forename: Eric
authors.initials: E
authors.lastname: Sarpong
authors.forename: Timothy M
authors.initials: TM
authors.lastname: Klein
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000517:prevention & control; D003198:Computer Simulation; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D012680:Sensitivity and Specificity