Open AccessAn empirical evaluation of alternative approaches to adjusting for attrition when analyzing longitudinal survey data on young adults' substance use trajectories
Author(s) -
Si Yajuan,
West Brady T.,
Veliz Philip,
Patrick Megan E.,
Schulenberg John E.,
Kloska Deborah D.,
TerryMcElrath Yvonne M.,
McCabe Sean E.
Publication year - 2022
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.1916
Subject(s) - attrition , covariate , selection bias , longitudinal study , sample (material) , longitudinal data , estimation , psychology , sample size determination , missing data , descriptive statistics , statistics , econometrics , computer science , medicine , mathematics , data mining , chemistry , management , dentistry , chromatography , economics
Objectives Longitudinal survey data allow for the estimation of developmental trajectories of substance use from adolescence to young adulthood, but these estimates may be subject to attrition bias. Moreover, there is a lack of consensus regarding the most effective statistical methodology to adjust for sample selection and attrition bias when estimating these trajectories. Our objective is to develop specific recommendations regarding adjustment approaches for attrition in longitudinal surveys in practice. Methods Analyzing data from the national U.S. Monitoring the Future panel study following four cohorts of individuals from modal ages 18 to 29/30, we systematically compare alternative approaches to analyzing longitudinal data with a wide range of substance use outcomes, and examine the sensitivity of inferences regarding substance use prevalence and trajectories as a function of college attendance to the approach used. Results Our results show that analyzing all available observations in each wave, while simultaneously accounting for the correlations among repeated observations, sample selection, and attrition, is the most effective approach. The adjustment effects are pronounced in wave‐specific descriptive estimates but generally modest in covariate‐adjusted trajectory modeling. Conclusions The adjustments can refine the precision, and, to some extent, the implications of our findings regarding young adult substance use trajectories.