Competing-risks survival regression provides a useful alternative to Cox regression in the presence of one or more competing risks. For example, say that you are studying the time from initial treatment for cancer to recurrence of cancer in relation to the type of treatment administered and demographic factors. Death is a competing event: the person under treatment may die, impeding the occurence of the event of interest, recurrence of cancer. Unlike censoring, which merely obstructs you from viewing the event, a competing event prevents the event of interest from occurring altogether. Depending on the scope of your statistical inference, your analysis may need to be adjusted for competing risks. Stata’s new stcrreg command implements competing-risks regression based on Fine and Gray’s proportional subhazards model. This talk will focus on that new command, and compare the method of Fine and Gray to a method based on directly modeling cause-specific hazards. Regardless of method, the focus is on estimating the cumulative incidence function (CIF) for the event of interest in the presence of competing events.
