Multiple imputation of cognitive performance as a repeatedly measured outcome
- 502 Downloads
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.
KeywordsBias Cognitive function Epidemiologic methods Missing data Multiple imputation Prospective study
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.
- 1.Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002;7(2):147–177. http://www.ncbi.nlm.nih.gov/pubmed/12090408. Accessed 22 Mar 2015.
- 11.Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11(5):550–560. http://www.ncbi.nlm.nih.gov/pubmed/10955408. Accessed 25 Aug 2015.
- 12.Rabbitt P, Diggle P, Holland F, McInnes L. Practice and drop-out effects during a 17-year longitudinal study of cognitive aging. J Gerontol B Psychol Sci Soc Sci. 2004;59(2):P84–P97. http://www.ncbi.nlm.nih.gov/pubmed/15014091. Accessed 13 Aug 2015.
- 15.Gerstorf D, Herlitz A, Smith J. Stability of sex differences in cognition in advanced old age: the role of education and attrition. J Gerontol B Psychol Sci Soc Sci. 2006;61(4):P245–P249. http://www.ncbi.nlm.nih.gov/pubmed/16855037. Accessed 13 Aug 2015.
- 18.The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol. 1989;129(4):687–702. http://www.ncbi.nlm.nih.gov/pubmed/2646917.
- 19.Knopman DS, Ryberg S. A verbal memory test with high predictive accuracy for dementia of the Alzheimer type. Arch Neurol. 1989;46(2):141–145. http://www.ncbi.nlm.nih.gov/pubmed/2916953.
- 20.Wechsler D. Manual for the wechsler adult intelligence scale, Revised. 1981.Google Scholar
- 21.Benton A, Hamsher K. Multilingual aphasia examination. 2nd ed. Oowa City: AJA Associates; 1989.Google Scholar
- 22.Brandt J, Spencer M, Folstein M. The telephone interview for cognitive status. Neuropsychiatry Neuropsychol Behav Neurol. 1988;1(2):111–8.Google Scholar
- 24.Plassman BLPD, Newman TTBS, Welsh KAPD, Helms MBS, Breitner JCS. Properties of the telephone interview for cognitive status: application in epidemiological and longitudinal studies. Neuropsychiatry Neuropsychol Behav Neurol. 1994;7(3):235–41.Google Scholar
- 25.Manual 19. Surveillance of Dementia in the ARIC Cohort.; 2015.Google Scholar
- 28.Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. Hoboken: Wiley; 2002.Google Scholar
- 33.Sauvaget C, Tsuji I, Haan MN, Hisamichi S. Trends in dementia-free life expectancy among elderly members of a large health maintenance organization. Int J Epidemiol. 1999;28(6):1110–1118. http://www.ncbi.nlm.nih.gov/pubmed/10661655. Accessed 27 June 2016.
- 34.Chaix B, Evans D, Merlo J, Suzuki E. Commentary: weighing up the dead and missing: reflections on inverse-probability weighting and principal stratification to address truncation by death. Epidemiology. 2012;23(1):129–131; discussion 132–137. doi: 10.1097/EDE.0b013e3182319159.
- 35.Stuart EA, Azur M, Frangakis C, Leaf P. Multiple imputation with large data sets: a case study of the Children’s Mental Health Initiative. Am J Epidemiol. 2009;169(9):1133–1139. doi: 10.1093/aje/kwp026.
- 39.Brand JPL. Development, implementation and evaluation of multiple imputation strategies for the statistical analysis of incomplete data sets. 1999.Google Scholar
- 40.Scharfstein DO, Irizarry RA. Generalized additive selection models for the analysis of studies with potentially nonignorable missing outcome data. Biometrics. 2003;59(3):601–613. http://www.ncbi.nlm.nih.gov/pubmed/14601761. Accessed 23 Aug 2015.
- 41.Greenland S. Basic methods for sensitivity analysis of biases. Int J Epidemiol. 1996;25(6):1107–1116. http://www.ncbi.nlm.nih.gov/pubmed/9027513. Accessed 23 Aug 2015.