STATA – Interval-censored survival data—model fitting and beyond

What are interval-censored data?

Survival data often contain censored observations for which time to an event of interest is not observed exactly. Censored observations can be right-censored, left-censored, or interval-censored. An observation is right-censored if we know that the event of interest happened after the observed time. It is left-censored if we know that the event happened before the observed time. It is interval-censored if we know only that the event happened within some observed time interval. The term interval-censored data is used in general to refer to data that might be right-censored, left-censored, or interval-censored.

Interval-censored survival data arise in many areas, including medical, epidemiological, financial, and sociological studies. A common example is a clinical trial where patients are tested or measured periodically to evaluate if the event of interest has happened. We may not observe the exact time of the event, but we know that it happened before an evaluation, after an evaluation, or between two evaluations. The same applies to many other examples, such as unemployment duration in economic data, time of weaning in demographic data, or time to obesity in epidemiological data. Ignoring interval-censoring may lead to biased estimates.

In Stata, we can fit parametric models to interval-censored survival-time data using the stintreg command. stintreg supports different distributions and parameterizations, as well as the modeling of ancillary parameters and stratification. The command can analyze data that include all types of censoring, and it can also analyze current status data in which the event of interest is known to occur only before or after an observed time.

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