Modeling Interval-Censored Event-Time Data with Stata

Do you have event-time data that you would like to model, but are unsure exactly when the events occurred? In survival analysis, interval-censored event-time data arise when the event of interest is not observed precisely but is known to have occurred within a specific time interval. Stata 17 introduced the stintcox command to fit genuine semiparametric Cox models for such data, and Stata 18 expanded its capabilities by adding support for time-varying covariates (TVCs). Building on this, Stata 19 introduces the new stmgintcox command, enabling the modeling of interval-censored multiple-event data while accounting for potential correlations between event times across different event types. In this presentation, we will describe the fundamental types of interval-censored data and demonstrate how to fit the semiparametric Cox proportional hazards model using the stintcox command. We will provide examples using single-record and multiple-record-per-subject datasets and show how to incorporate TVCs. Additionally, we will discuss how to interpret and plot results, and how to assess the proportional hazards assumption. Finally, we will show you how to fit a marginal Cox proportional hazards model to interval-censored multiple-event data and perform a more powerful test for common covariate effects across all events.