1. Attrition and its effects – example from analysis of the MRC cognitive function and aging study Fiona Matthews MRC Biostatistics Unit

2. Outline Background - description of the MRC Cognitive Function and Ageing study (MRC CFAS) Attrition differences Analysis methods Results Implications

3. Aim Estimate population cognition levels over time But same principles arise in any outcome related to cognitive ability / dementia outcome

4. Population cognition

5. Why do we need to consider attrition? Individuals who have poor cognition less likely to take part at the next interview (Missing at random) Individuals who have poor cognition less likely to take part at this interview (informative dropout)

6. Attrition in the elderly Attrition increases with age Attrition increases with cognitive impairment Attrition decreases with education ALL these factors are associated with outcome of interest

7. MRC CFAS 13,004 individuals (5 identical centres) Aged 65 years and above in 1991, equal numbers of 65-75 and 75+ Rural and urban sites Population sampling including institutions Interviewed over ten years of follow-up ~ 80% response rate at each stage

8. The comparison of dementia and cognitive impairment across differing sites of:- Principal aims Prevalence Incidence Longitudinal patterns key dimensions Biological underpinning Implications for policy

9. MRC CFAS

10. Cognition Cognition measured at every interview (using Mini Mental State examination – MMSE) Individuals with poor cognition less likely to be successfully interviewed Study design also requires measurements to be weighted

11. INCIDENCE SCREEN N=7175 PREVALENCE SCREEN N=13004 MRC CFAS study design FOLLOW UP N=920 SCREEN AND ASSESSMENT N=1651 SCREEN AND ASSESSMENT N=3145 FOLLOW UP N=920 SCREEN AND ASSESSMENT N=1743 Inc. ASSESSMENT N=1463 Pr. ASSESSMENT N=2640 Initial interview of ~2,500 in each of the centres A 20% subsample, weighted to more cognitively frail. Dementia diagnosis at this interview At 2 years a new assessment interview in all those currently with an assessment interview. A opportunistic one year interview was also undertaken. All individuals without an assessment at baseline were re-screened A new 20% subsample, weighted to cognitively impaired All individuals who had previously been assessed were re-interviewed at 6-8 years. Recently completed 10 year follow-up

12. INCIDENCE SCREEN N=7175 PREVALENCE SCREEN N=13004 MRC CFAS study design FOLLOW UP N=920 SCREEN AND ASSESSMENT N=1651 SCREEN AND ASSESSMENT N=3145 FOLLOW UP N=920 SCREEN AND ASSESSMENT N=1743 Inc. ASSESSMENT N=1463 Pr. ASSESSMENT N=2640

13. Attrition effects in CFAS

14. Attrition methods MAR and Informative missing models can be modelled using –Full likelihood models –Inverse probability weighting –General linear growth models –Mean score imputation

15. Analysis method Missing at random mechanism –First model missing data mechanism Output probabilities become weights for individuals who are seen (can be multiplied by study weights)

16. Informative missing Similar process for MAR Model for missing also includes coefficient of current cognition and last cognition Iterate the missing data model that removes reliance on previous cognition Use these probabilities as weights

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18. Conclusions Missing data – even informative missing data does not jeoperdise conclusions Understanding of the missing data risk factors are essential for the modelling process