When does attrition lead to biased estimates of alcohol consumption? Bias analysis for loss to follow‐up in 30 longitudinal cohorts. Issue 4 (13th July 2020)

• Record Type: Journal Article
• Authors: Keyes, Katherine M.; Jager, Justin; Platt, Jonathan; Rutherford, Caroline; Patrick, Megan E.; Kloska, Deborah D.; Schulenberg, John
• Is Part Of: International journal of methods in psychiatric research. Volume 29:Issue 4(2020)
• Page Start: 1
• Page End: 9
• Publication Date: 2020-07-13
• Subjects: adolescents -- alcohol -- attrition; Psychiatry -- Research -- Methodology -- Periodicals; Psychiatry -- Periodicals; 616.890072
• DOI: 10.1002/mpr.1842
• Languages: English
• ISSNs: 1049-8931

Abstract

Abstract: Objectives: Survey nonresponse has increased across decades, making the amount of attrition a focus in generating inferences from longitudinal data. Use of inverse probability weights [IPWs] and other statistical approaches are common, but residual bias remains a threat. Quantitative bias analysis for nonrandom attrition as an adjunct to IPW may yield more robust inference. Methods: Data were drawn from the Monitoring the Future panel studies [twelfth grade, base‐year: 1976–2005; age 29/30 follow‐up: 1987–2017, N = 73, 298]. We then applied IPW imputation in increasing percentages, assuming varying risk differences [RDs] among nonresponders. Measurements included past‐two‐week binge drinking at base‐year and every follow‐up. Demographic and other correlates of binge drinking contributed to IPW estimation. Results: Attrition increased: 31.14%, base‐year 1976; 61.33%, base‐year 2005. The magnitude of bias depended not on attrition rate but on prevalence of binge drinking and RD among nonrespondents. The probable range of binge drinking among nonresponders was 12–45%. In every scenario, base‐year and follow‐up binge drinking were associated. The likely range of true RDs was 0.14 [95% CI: 0.11–0.17] to 0.28 [95% CI: 0.25–0.31]. Conclusions: When attrition is present, the amount of attrition alone is insufficient to understand contribution to effect estimates. We recommend including bias analysis in longitudinal analyses.