Abstract
Neuroimaging research is usually conducted in volunteers who meet a priori selection criteria. Selection/volunteer bias is assumed but cannot be assessed. During an ongoing population-based cohort study of 1982 older adults, we asked 1702 active participants about their interest in undergoing a research brain scan. Compared with those not interested, the 915 potentially interested individuals were significantly younger, more likely to be male, better educated, generally healthier, and more likely to be cognitively intact and dementia-free. In 48 of the interested individuals, we conducted a previously reported pilot structural magnetic resonance imaging (sMRI) study modelling mild cognitive impairment (MCI) vs. normal cognition, and Clinical Dementia Rating (CDR) = 0.5 vs. CDR = 0, as a function of sMRI atrophy ratings. We now compare these 48 individuals (1) with all interested participants, to assess selection bias; (2) with all who had been asked about their interest, to assess volunteer bias; and (3) with the entire study cohort, to assess attrition bias from those who had dropped out before the question was asked. Using these data in propensity score models, we generated weights which we applied to logistic regression models reanalyzing the data from the pilot sMRI study. These weighted models adjusted, in turn, for selection bias, interest/volunteer bias, and attrition bias. They show fewer regions of interest to be associated with MCI/ CDR than were in the original unweighted models. When study participants are drawn from a well-characterized population, they can be compared with non-participants, and the information used to correct study results for potential bias and thus provide more generalizable estimates.
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An, A. B. (2002). Performing logistic regression on survey data with the new SURVEYLOGISTIC procedure. Proceedings of the Twenty-Seventh Annual SAS® Users Group International Conference. pp. 258–27. Cary, NC: SAS Institute Inc., Paper 258–27.
Becker, J. T., Duara, R., Lee, C. W., Teverovsky, L., Snitz, B. E., Chang, C. C., & Ganguli, M. (2012). Cross-validation of brain structural biomarkers and cognitive aging in a community-based study. International Psychogeriatrics, 24, 1065–1075.
Bryan, R. N., Cai, J., Burke, G., et al. (1999). Prevalence and anatomic characteristics of infarct-like lesions on MR images of middle-aged adults: the Atherosclerosis Risk in Communities Study. American Journal of Neuroradiology, 20, 1273–1280.
De Groot, J. C., de Leeuw, F.-E., Oudkerk, M., Hofman, A., Jolles, J., & Breteler, M. M. B. (2000). Cerebral white matter lesions and depressive symptoms in elderly adults. Archives of General Psychiatry, 57, 1071–1076.
DeCarli, C., Massaro, J., Harvey, D., Hald, J., Tullberg, M., Au, R., et al. (2005). Measures of brain morphology and infarction in the Framingham Heart Study: establishing what is normal. Neurobiology of Aging, 26, 491–510.
Department of Health, Education, and Welfare, Belmont Report. (1979). The Belmont Report: Ethical principles and guidelines for the protection of human subjects of research. hhs.gov/ohrp/humansubjects/guidance/belmont.html.
Duara, R., Loewenstein, D. A., Shen, Q., Barker, W., Varon, D., Grieg, M. T., et al. (2013). The utility of age-specific cut-offs for visual rating of medial temporal atrophy in classifying Alzheimer’s disease, MCI, and cognitively normal elderly subjects. Frontiers in Aging Neuroscience, 5, 47.
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.
Freedman, D. A., & Berk, R. A. (2008). Weighting regressions by propensity scores. Evaluation Review, 32, 392–409.
Ganguli, M., Lytle, M. E., Reynolds, M. D., & Dodge, H. H. (1998). Random versus volunteer selection for a community-based study. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 53, M39–M46.
Ganguli, M., Snitz, B. E., Vander, B. J., & Chang, C.-C. H. (2009). How much do depressive symptoms affect cognition at the population level? The Monongahela-Youghiogheny Healthy Aging Team (MYHAT) study. International Journal of Geriatric Psychiatry, 24, 1277–1284.
Ganguli, M., Chang, C.-C. H., Snitz, B. E., Saxton, J. A., Vander, B. J., & Lee, C. W. (2010). Prevalence of mild cognitive impairment by multiple classifications: the MYHAT Project. The American Journal of Geriatric Psychiatry, 8, 674–683.
Gorkiewicz, M. (2009). Using propensity score with receiver operating characteristics (ROC) and Bootstrap to evaluate effect size in observational studies. Biocybernetics and Biomedical Engineering, 29, 41–61.
Havlik, R. J., Foley, D. J., Sayer, B., Masaki, K., White, L., & Launer, L. J. (2002). Variability in midlife systolic blood pressure is related to late-life brain white matter lesions: the Honolulu- Asia Aging Study. Stroke, 33, 26–30.
Jaramillo, S. A., Felton, D., Andrews, L., Desiderio, L., Hallarn, R. K., Jackson, S. D., et al. (2007). Women’s Health Initiative Memory Study Research Group. Enrollment in a brain magnetic resonance study: results from the Women’s Health Initiative Memory Study Magnetic Resonance Imaging Study (WHIMS-MRI). Academic Radiology, 14, 603–612.
Kukull, W. A., & Ganguli, M. (2012). Generalizability: the trees, the forest, and the low-hanging fruit. Neurology, 78(23), 1886–1891.
Longstreth, W. T., Manolio, T. A., Arnold, A., Burke, G. L., Bryan, N., Jungreis, C. A., et al. (1996). Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. Stroke, 27, 1274–1282.
Morris, J. C. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43, 2412–2414.
Oswald, L. M., Wand, G. S., Zhu, S., & Selby, V. (2013). Volunteerism and self-selection bias in human positron emission tomography neuroimaging research. Brain Imaging and Behavior, 7, 163–176.
Pfeffermann, D. (1996). The use of sampling weights for survey data analysis. Statistical Methods in Medical Research, 5, 239–261.
Stürmer, T., Schneeweiss, S., Avorn, J., & Glynn, R. J. (2005). Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. American Journal of Epidemiology, 162, 279–289.
Urs, R., Potter, E., Barker, W., Appel, J., Loewenstein, D. A., Zhao, W., & Duara, R. (2009). Visual rating system for assessing magnetic resonance images: a tool in the diagnosis of mild cognitive impairment and Alzheimer disease. Journal of Computer Assisted Tomography, 33(1), 73–78.
Acknowledgments
The work reported here was supported in part by grants # R01 AG023651, K07 AG044395, K23AG083479 from the National Institute on Aging, US Department of Health and Human Services.
Conflict of interest
Authors Ganguli, Lee, Hughes, Jakubcak, Snitz, Chang, and Duara declare no conflicts of interest. Authors Ganguli, Lee, Hughes, Jakubcak, and Chang are supported by grant # R01 AG023651 from the National Institute on Aging. Dr. Ganguli is also supported by grant # K07 AG044395, Dr. Snitz is supported by grant # K23 AG08347.
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Ganguli, M., Lee, CW., Hughes, T. et al. Who wants a free brain scan? Assessing and correcting for recruitment biases in a population-based sMRI pilot study. Brain Imaging and Behavior 9, 204–212 (2015). https://doi.org/10.1007/s11682-014-9297-9
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DOI: https://doi.org/10.1007/s11682-014-9297-9