Attrition bias related to missing outcome data: A longitudinal simulation study

TitleAttrition bias related to missing outcome data: A longitudinal simulation study
Publication TypeJournal Article
Year of Publication2018
AuthorsLewin A., Brondeel R., Benmarhnia T., Thomas F., Chaix B.
JournalEpidemiology
Volume29
Pagination87-95
Date Published2018/01
Type of ArticleArticle
ISBN Number1044-3983
Accession NumberWOS:000417683700018
Keywordsassociations; characteristics; drop-out; efficiency; environment; inference; multiple imputation; neighborhood; record cohort; risk-factors; socioeconomic
Abstract

Background: Most longitudinal studies do not address potential selection biases due to selective attrition. Using empirical data and simulating additional attrition, we investigated the effectiveness of common approaches to handle missing outcome data from attrition in the association between individual education level and change in body mass index (BMI). Methods: Using data from the two waves of the French RECORD Cohort Study (N = 7,172), we first examined how inverse probability weighting (IPW) and multiple imputation handled missing outcome data from attrition in the observed data (stage 1). Second, simulating additional missing data in BMI at follow-up under various missing-at-random scenarios, we quantified the impact of attrition and assessed how multiple imputation performed compared to complete case analysis and to a perfectly specified IPW model as a gold standard (stage 2). Results: With the observed data in stage 1, we found an inverse association between individual education and change in BMI, with complete case analysis, as well as with IPW and multiple imputation. When we simulated additional attrition under a missing-at-random pattern (stage 2), the bias increased with the magnitude of selective attrition, and multiple imputation was useless to address it. Conclusions: Our simulations revealed that selective attrition in the outcome heavily biased the association of interest. The present article contributes to raising awareness that for missing outcome data, multiple imputation does not do better than complete case analysis. More effort is thus needed during the design phase to understand attrition mechanisms by collecting information on the reasons for dropout.

DOI10.1097/ede.0000000000000755
Short TitleEpidemiology
Student Publication: 
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