1 item has been added to your cart.
| Duration: | 1 hour |
|---|---|
| Where: | Join us from anywhere! |
| Cost: | Free—but registrations are limited |
In survival analysis, interval-censored event-time data occur when the event of interest is not always observed exactly but is known to lie within some time interval. These types of data arise in many areas, including medical, epidemiological, economic, financial, and sociological studies. Ignoring interval-censoring will often lead to biased estimates.
A semiparametric Cox proportional hazards regression model is used routinely to analyze uncensored and right-censored event-time data. It is also appealing for interval-censored data because it does not require any parametric assumptions about the baseline hazard function. Also, under the proportional-hazard assumption, the hazard ratios are constant over time.
Semiparametric estimation of interval-censored event-time data is challenging because none of the event times are observed exactly. Thus, "semiparametric" modeling of these data often resorted to using spline methods or piecewise-exponential models for the baseline hazard function. Genuine semiparametric modeling of interval-censored event-time data was not available until recent methodological advances, which are implemented in the stintcox command.
In this webinar, we describe basic types of interval-censored data and demonstrate how to fit the semiparametric Cox proportional hazards model to these data using Stata's new stintcox command. We also discuss how to interpret and plot results and how to graphically assess the proportional-hazard assumption.
The webinar is free, but you must register to attend. Registrations are limited so register soon.
We will send you an email with instructions on how to join prior to the start of the webinar.
Xiao Yang is a Principal Statistician and Software Developer at StataCorp LLC. Xiao has been developing Stata since 2012. Her interests include survival analysis, longitudinal analysis, Bayesian analysis, and multilevel mixed-effects models. She has a bachelor's degree in computer science from the University of Electronic Science and Technology of China, a master's degree in mathematics from Southeast Missouri State University, and a master's degree in statistics from the University of Iowa.
