title: One size fits all? Why we need more sophisticated analytical methods in the explanation of trajectories of cognition in older age and their potential risk factors.
abstract: BACKGROUND
  Cognitive decline in old age varies among individuals. The identification of groups of individuals with similar patterns of cognitive change over time may improve our ability to see whether the effect of risk factors is consistent across groups.
  
  
  METHODS
  Whilst accounting for the missing data, growth mixture models (GMM) were fitted to data from four interview waves of a population-based longitudinal study of aging, the Cambridge City over 75 Cohort Study (CC75C). At all interviews global cognition was assessed using the Mini-mental State Examination (MMSE).
  
  
  RESULTS
  Three patterns were identified: a slow decline with age from a baseline of cognitive ability (41% of sample), an accelerating decline from a baseline of cognitive impairment (54% of sample) and a steep constant decline also from a baseline of cognitive impairment (5% of sample). Lower cognitive scores in those with less education were seen at baseline for the first two groups. Only in those with good performance and steady decline was the effect of education strong, with an increased rate of decline associated with poor education. Good mobility was associated with higher initial score in the group with accelerating change but not with rate of decline.
  
  
  CONCLUSIONS
  Using these analytical methods it is possible to detect different patterns of cognitive change with age. In this investigation the effect of education differs with group. To understand the relationship of potential risk factors for cognitive decline, careful attention to dropout and appropriate analytical methods, in addition to long-term detailed studies of the population points, are required.
authors.affiliation: MRC Biostatistics Unit, Institute of Public Health, University of Cambridge, Cambridge, U.K. graciela.muniz@mrc-bsu.cam.ac.uk
authors.forename: Graciela Muniz
authors.initials: GM
authors.lastname: Terrera
authors.forename: Carol
authors.initials: C
authors.lastname: Brayne
authors.forename: Fiona
authors.initials: F
authors.lastname: Matthews
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D003071:Cognition; D003072:Cognition Disorders / Q000175:diagnosis / Q000209:etiology / Q000523:psychology*; D018450:Disease Progression; D004522:Educational Status; D005260:Female; D006801:Humans; D008297:Male; D008960:Models, Psychological; D011569:Psychiatric Status Rating Scales / Q000592:standards; D011597:Psychomotor Performance; D012307:Risk Factors; D012737:Sex Factors