When does attrition lead to biased estimates of alcohol consumption? Bias analysis for loss to follow‐up in 30 longitudinal cohorts

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

Alcohol use and the COVID-19 pandemic: Historical trends in drinking, contexts, and reasons for use among U.S. adults

“…One important limitation was attrition, particularly differential attrition with respect to substance use, with attrition being higher among those who use substances ( McCabe and West, 2016 ). However, the use of attrition weights helped mitigate this limitation ( Keyes et al, 2020 ). Additional limitations and caveats include (1) the reliance on self-report data; (2) the exclusion of youth who dropped out or were not in high school; (3) the most recent time point being during the first year of the pandemic; and (4) lack of accounting for local differences in pandemic-related disease transmission and public health response.…”

Exploring the role of social capital, self-efficacy and social contagion in shaping lifestyle and mental health among students representing the future healthcare workforce in Palestine: social cohort study protocol

“…Subsequently, strategies to increase the retention rate of such participants will be implemented 49 50. Inverse probability weighting will be applied to ensure that the analyses are unbiased in spite of attrition 48 51 52…”

Longitudinal Analysis of Substance Use Disorder Symptom Severity at Age 18 Years and Substance Use Disorder in Adulthood

“…The mean (SD) retention rate from first follow-up to age 50 follow-up (among those who could have made it to age 50) is 63.6% (10.9%). To help correct for potential attrition bias, and to be consistent with other MTF panel analyses, 13 , 19 , 20 we incorporated attrition weights to account for respondent characteristics associated with nonresponse at follow-up. The MTF study design, protocol, and sampling methods are described in greater detail elsewhere.…”