Survival analysis with interval-censored data

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Do you have event-time data that you would like to model, but you're unsure exactly when the events occurred?

In survival analysis, interval-censored event-time data arise when the event of interest is not always observed exactly 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 webinar, 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 both 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.