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Accounting for Bias Due to Selective Attrition: The Example of Smoking and Cognitive Decline

Weuve, Jennifer; Tchetgen Tchetgen, Eric J.; Glymour, M. Maria; Beck, Todd L.; Aggarwal, Neelum T.; Wilson, Robert S.; Evans, Denis A.; Mendes de Leon, Carlos F.

doi: 10.1097/EDE.0b013e318230e861

Background: Selective attrition may introduce bias into analyses of the determinants of cognitive decline. This is a concern especially for risk factors, such as smoking, that strongly influence mortality and dropout. Using inverse-probability-of-attrition weights, we examined the influence of selective attrition on the estimated association of current smoking (vs. never smoking) with cognitive decline.

Methods: Chicago Health and Aging Project participants (n = 3713), aged 65–109 years, who were current smokers or never- smokers, underwent cognitive assessments up to 5 times at 3-year interval. We used pooled logistic regression to fit predictive models of attrition due to death or study dropout across the follow-up waves. With these models, we computed inverse-probability-of-attrition weights for each observation. We fit unweighted and weighted, multivariable-adjusted generalized-estimating-equation models, contrasting rates of change in cognitive scores in current versus never-smokers. Estimates are expressed as rates of change in z score per decade.

Results: During the 12 years of follow-up, smokers had higher mortality than never-smokers (hazard ratio = 1.93 [95% confidence interval = 1.67 to 2.23]). Higher previous cognitive score was associated with increased likelihood of survival and continued participation. In unweighted analyses, current smokers' cognitive scores declined 0.11 standard units per decade more rapidly than never-smokers' (95% CI = −0.20 to −0.02). Weighting to account for attrition yielded estimates that were 56% to 86% larger, with smokers' estimated 10-year rate of decline up to 0.20 units faster than never-smokers' (95% CI = −0.36 to −0.04).

Conclusions: Estimates of smoking's effects on cognitive decline may be underestimated due to differential attrition. Analyses that weight for the inverse probability of attrition help compensate for this attrition.

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From the aRush Institute for Healthy Aging and Department of Internal Medicine, Rush University Medical Center, Chicago, IL; Departments of bEpidemiology and Biostatistics and cDepartment of Society, Human Development and Health, Harvard School of Public Health, Boston, MA; dRush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL; and Departments of eNeurological Sciences and fDepartment of Behavioral Sciences, Rush University Medical Center, Chicago, IL.

Editors' note: Related articles appear on pages 129 and 132.

Conflicts of interest and sources of funding: Dr. Weuve was supported in part by funds from NIH-NIEHS grant R21 ES016829. Dr. Glymour was supported in part by funds from NIH-NIA grant R21 AG34385. The authors reported no other financial interests related to this research.

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Correspondence: Jennifer Weuve, Rush Institute for Healthy Aging, Rush University Medical Center, 1645 W. Jackson Boulevard, Suite 675, Chicago, IL 60612. E-mail:

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