Objectives: Longitudinal studies are a major tool for public health research, but their value can be undermined by attrition. Identification of factors associated with attrition through modeling depends on the efficient use of data and is conditional on modeling assumptions being met. The primary aim of this study was to compare the performance of four models in analyzing attrition risk. Study Design and Setting: Data from participants who were lost to follow-up from The Nambour Skin Cancer Study between 1992 and 2000 were analyzed using logistic and survival models, for all-cause and nondeath attritions. Results: During follow-up, 321 (19.8%) of 1,621 participants were lost to follow-up; 70 (4.3%) because of death and 251 (15.5%) for other reasons. Using survival models showed skin cancer diagnosis to be associated with increased all-cause attrition (hazard ratio: 2.3; 95% confidence interval [95% CI]: 1.5, 3.4) and nondeath attrition (subhazard ratio: 1.9; 95% CI: 1.0, 3.3). Using logistic regression resulted in inverse associations being observed for both all-cause attrition (odds ratio [OR]: 0.7; 95% CI: 0.5, 1.1) and nondeath attrition (OR: 0.5; 95% CI: 0.3, 1.0). Conclusion: These results demonstrate the relative inadequacy of a logistic as opposed to a survival approach when analyzing attrition risk in the presence of time-varying covariates and multiple timepoints.