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Publication Detail

Title: Accounting for bias due to selective attrition: the example of smoking and cognitive decline.

Authors: 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

Published In Epidemiology, (2012 Jan)

Abstract: 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.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.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).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.

PubMed ID: 21989136 Exiting the NIEHS site

MeSH Terms: Aged; Aged, 80 and over; Bias*; Cognition Disorders/epidemiology; Cognition Disorders/etiology*; Female; Humans; Longitudinal Studies; Male; Markov Chains; Models, Statistical; Patient Dropouts/statistics & numerical data*; Risk Factors; Smoking/adverse effects*; Smoking/epidemiology; Smoking/mortality

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