We evaluated alternative approaches to assessing and correcting for nonresponse bias in a longitudinal survey. We considered the changes in substance-use outcomes over a 3-year period among young adults aged 18â€”24 years (nÂ =Â 5,199) in the United States, analyzing data from the National Epidemiologic Survey on Alcohol and Related Conditions. This survey collected a variety of substance-use information from a nationally representative sample of US adults in 2 waves: 2001â€”2002 and 2004â€”2005. We first considered nonresponse rates in the second wave as a function of key substance-use outcomes in wave 1. We then evaluated 5 alternative approaches designed to correct for nonresponse bias under different attrition mechanisms, including weighting adjustments, multiple imputation, selection models, and pattern-mixture models. Nonignorable attrition in a longitudinal survey can lead to bias in estimates of change in certain health behaviors over time, and only selected procedures enable analysts to assess the sensitivity of their inferences to different assumptions about the extent of nonignorability. We compared estimates based on these 5 approaches, and we suggest a road map for assessing the risk of nonresponse bias in longitudinal studies. We conclude with directions for future research in this area given the results of our evaluations.