Multiple imputation of cognitive performance as a repeatedly measured outcome.
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.
Department of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA. arawlin2@jhu.edu.
Andreea Monica
AM
Rawlings
Department of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Yingying
Y
Sang
Department of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Albert Richey
AR
Sharrett
Department of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Josef
J
Coresh
Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, MS, USA.
Michael
M
Griswold
Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
Anna Maria
AM
Kucharska-Newton
Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
Priya
P
Palta
Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
Lisa Miller
LM
Wruck
Department of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Alden Lawrence
AL
Gross
Department of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Jennifer Anne
JA
Deal
Department of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.|Department of Epidemiology and Biostatistics, George Washington University Milken Institute of Public Health, Washington, DC, USA.
Melinda Carolyn
MC
Power
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Karen Jean
KJ
Bandeen-Roche
D003071:Cognition; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D064887:Observational Studies as Topic; D017063:Outcome Assessment, Health Care; D011446:Prospective Studies; D012107:Research Design
Bias; Cognitive function; Epidemiologic methods; Missing data; Multiple imputation; Prospective study