Analysis of time-to-event data, otherwise known as survival analysis, is a common investigative tool in ophthalmic research. For example, time-to-event data is useful when researchers are interested in investigating how long it takes for an ocular condition to worsen or whether treatment can delay the development of a potentially vision-threatening complication. Its implementation requires a different set of statistical tools compared to those required for analyses of other continuous and categorial outcomes. In this installment of the Focus on Data series, we present an overview of selected concepts relating to analysis of time-to-event data in eye research. We introduce censoring, model selection, consideration of model assumptions, and best practice for reporting. We also consider challenges that commonly arise when analyzing time-to-event data in ophthalmic research, including collection of data from two eyes per person and the presence of multiple outcomes of interest. The concepts are illustrated using data from the Laser Intervention in Early Stages of Age-Related Macular Degeneration study and statistical computing code for Stata is provided to demonstrate the application of the statistical methods to illustrative data.