Accounting for Bias Due to Selective Attrition

The Example of Smoking and Cognitive Decline

Datos Bibliográficos

ID23323461
AutoresJennifer Weuve (0000-0003-0922-0256, Rush University Medical Center, autor de correspondencia), Eric J Tchetgen Tchetgen (0000-0002-8369-3900, Harvard University Press), M Maria Glymour (0000-0001-9644-3081, Rush University Medical Center), Todd Beck (0000-0001-8781-2105, Rush University Medical Center), Todd L Beck, Neelum T Aggarwal (0000-0002-4341-8023, Rush University Medical Center), Robert S Wilson (0000-0001-6334-0684), R J Wilson (0000-0002-8184-4103, Rush University Medical Center), Denis A Evans (0000-0003-4755-5401, Rush University Medical Center), Carlos F Mendes De Leon (0000-0003-3020-826X, Rush University)
Año2012
Volumen23
Número1
Páginas119-128
Fecha de publicación2012-01-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaEpidemiology (JOURNAL)
Identificadores de la revistaISSN: 1044-3983 • E-ISSN: 1531-5487
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/ede.0b013e318230e861
PMID21989136
PMCIDPMC3237815
OpenAlexW2093671650
IdiomaEN
Citas recibidas80
Referencias citadas50

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.

Attrition · Cognition · Cognitive decline · Confidence interval · Confounding · Hazard ratio · Inverse probability weighting · Logistic regression · Propensity score matching · Psychiatry · Statistics · Dementia and Cognitive Impairment Research · Demography · Frailty in Older Adults · Health disparities and outcomes · Internal Medicine · Mathematics · Medicine · Gerontology