Multiple imputation of cognitive performance as a repeatedly measured outcome

Andreea Monica Rawlings, Yingying Sang, Albert Richey Sharrett, Josef Coresh, Michael Griswold, Anna Maria Kucharska-Newton, Priya Palta, Lisa Miller Wruck, Alden Lawrence Gross, Jennifer Anne Deal, Melinda Carolyn Power, Karen Jean Bandeen-Roche

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Longitudinal studies of cognitive performance are sensitive to dropout, as participants experiencing cognitive deficits are less likely to attend study visits, which may bias estimated associations between exposures of interest and cognitive decline. Multiple imputation is a powerful tool for handling missing data, however its use for missing cognitive outcome measures in longitudinal analyses remains limited. We use multiple imputation by chained equations (MICE) to impute cognitive performance scores of participants who did not attend the 2011–2013 exam of the Atherosclerosis Risk in Communities Study. We examined the validity of imputed scores using observed and simulated data under varying assumptions. We examined differences in the estimated association between diabetes at baseline and 20-year cognitive decline with and without imputed values. Lastly, we discuss how different analytic methods (mixed models and models fit using generalized estimate equations) and choice of for whom to impute result in different estimands. Validation using observed data showed MICE produced unbiased imputations. Simulations showed a substantial reduction in the bias of the 20-year association between diabetes and cognitive decline comparing MICE (3–4 % bias) to analyses of available data only (16–23 % bias) in a construct where missingness was strongly informative but realistic. Associations between diabetes and 20-year cognitive decline were substantially stronger with MICE than in available-case analyses. Our study suggests when informative data are available for non-examined participants, MICE can be an effective tool for imputing cognitive performance and improving assessment of cognitive decline, though careful thought should be given to target imputation population and analytic model chosen, as they may yield different estimands.

Original languageEnglish
Pages (from-to)55-66
Number of pages12
JournalEuropean Journal of Epidemiology
Issue number1
StatePublished - Jan 1 2017

Bibliographical note

Funding Information:
The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C, with the ARIC carotid MRI examination funded by U01HL075572-01). Neurocognitive data is collected by U01 HL096812, HL096814, HL096899, HL096902, HL096917 from the NHLBI and the National Institute of Neurological Disorders and Stroke, and with previous brain MRI examinations funded by R01-HL70825 from the NHLBI. The authors thank the staff and participants of the ARIC study for their important contributions.

Publisher Copyright:
© 2016, Springer Science+Business Media Dordrecht.


  • Bias
  • Cognitive function
  • Epidemiologic methods
  • Missing data
  • Multiple imputation
  • Prospective study

ASJC Scopus subject areas

  • Epidemiology


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