Skip to main content Accessibility help

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

  • Graciela Muniz Terrera (a1), Carol Brayne (a2), Fiona Matthews (a1) and the CC75C Study Collaboration Group (a2)


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.

Conclusion: 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.


Corresponding author

Correspondence should be addressed to: Dr. Graciela Muniz Terrera, MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, U.K. Phone: +44 (0)1223 330393; Fax: +44 (0)1223 330365. Email:


Hide All
Ala, T. A., Hughes, L. F., Kyrouac, G. A., Ghobrial, M. W. and Elble, R. J. (2002). The Mini-Mental State exam may help in the differentiation of dementia with Lewy bodies and Alzheimer's disease. International Journal of Geriatric Psychiatry, 17, 503509.
Anstey, K. and Christensen, H. (2000). Education, activity, health, blood pressure and apolipoprotein E as predictors of cognitive change in old age: a review. Gerontology, 46, 163177.
Brayne, C. et al. (1996). Apolipoprotein E genotype in the prediction of cognitive decline and dementia in a prospectively studied elderly population. Dementia, 7, 169174.
Chatfield, M. D., Brayne, C. E. and Matthews, F. E. (2005). A systematic literature review of attrition between waves in longitudinal studies in the elderly shows a consistent pattern of dropout between differing studies. Journal of Clinical Epidemiology, 58, 1319.
Diggle, P. K. M. (1994). Informative dropout in longitudinal data analysis (with discussion). Applied Statistics, 43, 4993.
Fleming, J., Zhao, E., O'Connor, D. W., Pollitt, P. A. and Brayne, C. (2007). Cohort profile: the Cambridge City over-75s Cohort (CC75C). International Journal of Epidemiology, 36, 4046.
Folstein, M. F., Folstein, S. E. and McHugh, P. R. (1975). “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189198.
Katzman, R. (1993). Education and the prevalence of dementia and Alzheimer's disease. Neurology, 43, 1320.
Laird, N. M. (1988). Missing data in longitudinal studies. Statistics in Medicine, 7, 305315.
Laird, N. and Ware, J. (1982). Random effects models for longitudinal data. Biometrics, 38, 963974.
Liang, K. and Zeger, S. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 1322.
Matthews, F. E., Chatfield, M., Freeman, C., McCracken, C. and Brayne, C. (2004). Attrition and bias in the MRC cognitive function and ageing study: an epidemiological investigation. BMC.Public Health, 4, 12.
MRC CFAS (1998). Cognitive function and dementia in six areas of England and Wales: the distribution of MMSE and prevalence of GMS organicity levels in the MRC CFAS study. Psychological Medicine, 29, 319335.
Muniz Terrera, G., Matthews, F. E. and Brayne, C. (2008). A comparison of parametric models for the investigation of the shape of cognitive change in the older population. BMC Neurology, May 16, 816.
Muthén, B. and Muthén, L. (2004). Mplus: The Comprehensive Modeling Program for Applied Researchers – Users Guide. Los Angeles, CA.
Muthén, B. and Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463469.
Proust, C. and Jacqmin-Gadda, H. (2005). Estimation of linear mixed models with a mixture of distribution for the random effects. Computer Methods and Programs in Biomedicine, 78, 165173.
Ramswamy, V. (1993). An empirical pooling approach for estimating marketing mix elasticities with pims data. Marketing science, 12, 103124.
Schwartz, G. (1987). Estimating the dimension of a model. Annals of Statistics, 6, 461464.
Shaw, B., Krause, N., Liang, J. and Bennet, J. (2007). Age versus time since baseline as the time scale in the analysis of change. Journals of Gerontology Social Sciences, 62B, S203S204.
Small, B. J. and Backman, L. (2007). Longitudinal trajectories of cognitive change in preclinical Alzheimer's disease: a growth mixture modeling analysis. Cortex, 43, 826834.
Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8, 448460.
van Beijsterveldt, C. E., van Boxtel, M. P., Bosma, H., Houx, P. J., Buntinx, F. and Jolles, J. (2002). Predictors of attrition in a longitudinal cognitive aging study: the Maastricht Aging Study (MAAS). Journal of Clinical Epidemiology, 55, 216223.
Xuereb, J. H. et al. (2000). Neuropathological findings in the very old. Results from the first 101 brains of a population-based longitudinal study of dementing disorders. Annals of the New York Academy of Science, 903, 490496.
Yip, A. G., Brayne, C. and Matthews, F. E. (2006). Risk factors for incident dementia in England and Wales: the Medical Research Council Cognitive Function and Ageing Study. A population-based nested case-control study. Age and Ageing, 35, 154160.


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

  • Graciela Muniz Terrera (a1), Carol Brayne (a2), Fiona Matthews (a1) and the CC75C Study Collaboration Group (a2)


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed