Interval censoring occurs when the outcome of interest is time to onset of disease or time to disease progression and disease status is determined at intervals rather than continuously. Examples include tumor recurrence, the development of retinopathy in patients with diabetes, and the development of cirrhosis in patients with liver disease. These outcomes are based on biopsies, x-rays, special exams, or laboratory tests performed at regular intervals. This talk will review methods that have been developed to analyze these data, including parametric and semi-parametric methods and logistic regression using the complementary log-log link. These methods will be compared using existing clinical trial data sets and simulation and the effect of interval length will be evaluated.