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Correcting Bias Caused by Missing Data in the Estimate of the Effect of Apolipoprotein ε4 on Cognitive Decline

Published online by Cambridge University Press:  12 November 2014

Charles B. Hall Open the ORCID record for Charles B. Hall [Opens in a new window] ,
Richard B. Lipton ,
Mindy J. Katz  and
Cuiling Wang
Charles B. Hall*
Affiliation:
Department of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, New York Saul B. Korey Department of Neurology, Albert Einstein College of Medicine of Yeshiva University, Bronx, New York
Richard B. Lipton
Affiliation:
Department of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, New York Saul B. Korey Department of Neurology, Albert Einstein College of Medicine of Yeshiva University, Bronx, New York
Mindy J. Katz
Affiliation:
Saul B. Korey Department of Neurology, Albert Einstein College of Medicine of Yeshiva University, Bronx, New York
Cuiling Wang
Affiliation:
Department of Epidemiology and Population Health, Albert Einstein College of Medicine of Yeshiva University, Bronx, New York Saul B. Korey Department of Neurology, Albert Einstein College of Medicine of Yeshiva University, Bronx, New York
*
Correspondence and reprint requests to: Charles B. Hall, Department of Epidemiology and Population Health, Block 312, Albert Einstein College of Medicine, Bronx NY 10463. E-mail: [email protected]
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Abstract

Longitudinal administration of neuropsychological instruments are often used to assess age-related changes in cognition. Informative loss to follow-up may bias the results of these studies. Herein, we use auxiliary data to adjust for informative loss to follow-up. In the Einstein Aging Study, memory was assessed annually in a community sample of adults age 70+, free of dementia at baseline, using the free recall from the Free and Cued Selective Reminding Test, and via telephone using the Memory Impairment Screen for Telephone (the auxiliary data). Joint linear mixed models were used to assess how the effect of the APOE ε4 genotype may be affected by informative missingness in the in-person data. A total of 620 EAS participants contributed 2085 person years of follow-up to the analyses. Memory decline rates estimated in joint models were 19% greater in ε4 negative participants and 27% greater in ε4 positive participants compared to traditional approaches; the effect of APOE ε4 on memory decline was 37% greater. Joint modeling methods can help address bias caused by informative missing data in the estimation of the effect of risk factors on cognitive change, and may be applicable to a broader range of outcomes in longitudinal aging studies. (JINS, 2014, 20, 1–6)

Keywords

MemoryLongitudinal StudiesBias (Epidemiology)Risk FactorsApolipoprotein E4ModelsStatistical
Type
Brief Communication
Information
Journal of the International Neuropsychological Society , Volume 21 , Issue 1 , January 2015 , pp. 85 - 90
Copyright
Copyright © The International Neuropsychological Society 2014 

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References

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.CrossRefGoogle Scholar
Blessed, G., Tomlinson, B. E., & Roth, M. (1968). The association between quantitative measures of dementia and of senile change in the cerebral grey matter of elderly subjects. British Journal of Psychiatry, 114, 797–811.Google Scholar
Daniels, M. J., & Hogan, J. W. (2008). Missing data in longitudinal studies: Strategies for Bayesian modeling and sensitivity analysis. New York: Chapman & Hall.Google Scholar
Derby, C. A., Burns, L. C., Wang, C., Katz, M. J., Zimmerman, M. E., L’Italiend, G., & Lipton, R. B. (2013). Screening for predementia AD: Time dependent operating characteristics of episodic memory tests. Neurology, 80, 1307–1314.Google Scholar
Dodge, H. H., Wang, C. N., Chang, C. C., & Ganguli, M. (2011). Terminal decline and practice effects in non-demented older adults: The MoVIES project. Neurology, 77, 722–730.Google Scholar
Grober, E., & Buschke, H. (1987). Genuine memory deficits in dementia. Developmental Neuropsychology, 3, 13–36.CrossRefGoogle Scholar
Ibrahim, J. G., Lipsitz, S. R., & Horton, N. (2001). Using auxiliary data for parameter estimation with non-ignorably missing outcomes. Applied Statistics, 50, 361–373.Google Scholar
Katz, M. J., Lipton, R. B., Hall, C. B., Zimmerman, M. E., Sanders, A. E., Verghese, J., & Derby, C. A. (2012). Age-specific and sex-specific prevalence and incidence of mild cognitive impairment, dementia, and Alzheimer dementia in Blacks and Whites: A report from the Einstein aging study. Alzheimer Disease and Associated Disorders, 26, 335–343.Google Scholar
Lipton, R. B., Katz, M. J., Kuslansky, G., Sliwinski, M. J., Stewart, W. F., Verghese, J., & Buschke, H. (2003). Screening for dementia by telephone using the memory impairment screen. Journal of the American Geriatric Society, 51, 1382–1390.CrossRefGoogle ScholarPubMed
Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963–974.Google Scholar
Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York: Wiley.Google Scholar
Morris, J. C. (1993). The clinical dementia rating (CDR): Current version and scoring rules. Neurology, 43, 2412–2414.Google Scholar
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581–592.CrossRefGoogle Scholar
Sanders, A. E., Wang, C., Katz, M., Derby, C. A., Barzilai, N., Ozelius, L., & Lipton, R. B. (2010). Association of a functional polymorphism in the Cholesteryl Ester Transfer Protein (CETP) gene with memory decline and incidence of dementia. Journal of the American Medical Assnociation, 303, 150–158.CrossRefGoogle ScholarPubMed
Sliwinski, M. J., Stawski, R. S., Hall, C. B., Katz, M., Verghese, J., & Lipton, R. (2006). Distinguishing preterminal and terminal cognitive decline. European Psychologist, 11, 172–181.Google Scholar
Thorvaldsson, V., Hofer, S. M., Berg, S., Skoog, I., Sacuiu, S., & Johansson, B. (2008). Onset of terminal decline in cognitive abilities in individuals without dementia. Neurology, 71, 882–887.Google Scholar
Wang, C., & Hall, C. B. (2010). Correction of bias from non-random missing longitudinal data using auxiliary information. Statistics in Medicine, 29, 671–679.Google Scholar
Ward, A., Crean, S., Mercaldi, C. J., Collins, J. M., Boyd, D., Cook, M. N., & Arrighi, H. M. (2012). Prevalence of apolipoprotein E4 genotype and homozygotes (APOE e4/4) among patients diagnosed with Alzheimer’s Disease: A systematic review and meta-analysis. Neuroepidemiology, 38(1), 1–17.Google Scholar
Wilson, R. S., Beckett, L. A., Bienias, J. L., Evans, D. A., & Bennett, D. A. (2003). Terminal decline in cognitive function. Neurology, 60, 1782–1787.Google Scholar
Wilson, R. S., Beck, T. L., Bienias, J. L., & Bennett, D. A. (2007). Terminal cognitive decline: Accelerated loss of cognition in the last years of life. Psychosomatic Medicine, 69, 131–137.CrossRefGoogle ScholarPubMed