Open AccessWhen does attrition lead to biased estimates of alcohol consumption? Bias analysis for loss to follow‐up in 30 longitudinal cohorts
Author(s) -
Keyes Katherine M.,
Jager Justin,
Platt Jonathan,
Rutherford Caroline,
Patrick Megan E.,
Kloska Deborah D.,
Schulenberg John
Publication year - 2020
Publication title -
international journal of methods in psychiatric research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.275
H-Index - 73
eISSN - 1557-0657
pISSN - 1049-8931
DOI - 10.1002/mpr.1842
Subject(s) - attrition , binge drinking , demography , selection bias , alcohol consumption , longitudinal study , response bias , medicine , psychology , statistics , environmental health , poison control , injury prevention , alcohol , social psychology , mathematics , biochemistry , chemistry , dentistry , sociology
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.