ArticleEpidemiology (Cambridge, Mass.)2012
Accounting for bias due to selective attrition: the example of smoking and cognitive decline.
Article in Epidemiology (Cambridge, Mass.), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 304 papers, 1 of them a synthesis that pooled it.
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304 citing papers in PubMed, 1 synthesis or guideline pooled it, 491 citations in OpenAlex.
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- Association of Type 2 Diabetes Subgroups With Cognitive Status Without Modification From Lifestyle Intervention.The Journal of clinical endocrinology and metabolism · 2023Trial
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- Prenatal and early postnatal lead exposure and later adulthood BMI: The Saint Louis baby tooth - Later life health study.Environmental research · 2026Article
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- Rurality and long-term outcomes after critical illness.Annals of the American Thoracic Society · 2026Article
- Predictors of Follow-Up Engagement in an Adolescent Alcohol Longitudinal Cohort.Journal of studies on alcohol and drugs · 2026Article
- Smoking hot joint models for attrition bias-XMAR-ks the spot.American journal of epidemiology · 2026Article
- Factors Underlying Stroke Recovery Variation by Neighborhood Socioeconomic Status.JAMA network open · 2026Article
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244 more citing papers are in PubMed but not listed here.
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8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundSelective 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.
methodsChicago 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.
resultsDuring 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).
conclusionsEstimates 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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