I am new to survival analysis and am having difficulty choosing how to analyze my dataset.

The background is that I have a pretty big dataset that is in essence national level statistics for a disease that includes ~22,000 subjects, with slightly over 65K observations over five years.

The 'final state' of the patients can be:

  • Death (due to disease),
  • Death (due to other causes),
  • Cure,
  • Completed Treatment (but did not cure),
  • Fail Treatment (did not complete treatment for some reason),
  • Default (Lost-to-follow up), and
  • Continuing Treatment.
  • All told there are about ~2700 deaths, ~5000 cures, and ~7400 completed treatment patients, of course a whole lot of unknowns, and many co-variates which I can analyze (e.g. age, gender, homelessness, etc).

    Most of the literature I have read about competing risks talks about competing risks of death (e.g. 1 vs 2) but not cure (#3) as an alternate endpoint.  

    I'd like to analyze my data comparing/contrasting aspects of both cure and death and wondered if competing risks analysis is appropriate?

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