Predictors of 7-day abstinence from smoking were identified among participants in a randomized self-help smoking-cessation intervention trial conducted from 1985 to 1988 in Seattle, WA. Subjects were adult smokers belonging to a health maintenance organization who responded to an offer of free quitting assistance. Self-reported smoking status was assessed at 8, 16, and 24 months following enrollment. Predictors of abstinence were identified by longitudinal data analysis using Generalized Estimating Equations (GEEs), a modeling approach which handles repeated-measures data and accommodates time-dependent as well as time-independent covariates. Seventeen items emerged as significant (p < .05) predictors, with odds ratios ranging from 1.3 to 2.1. While much of the previous work in smoking-cessation research has focused on demographic and smoking history variables, results of this study indicate that emphasis should also be placed on psychosocial/motivational factors and quitting activities as important predictors of abstinence. Longitudinal data analysis represents a powerful technique for handling correlated (repeated measures) data, which may prove very useful for future studies of smoking cessation as well as other dynamic processes.