Re: st: Main effect for time-varying covariate

[Adam Olszewski <adam.olszewski@gmail.com>]

Hi,
This discussion is has now gone into a fairly nuanced issues of
interpretation of cause-specific-hazard and competing-risk survival
models, and one should note, after Latouche/Fine, that "there appears
to be no final consensus on how to analyze competing risks endpoints"
(J Clin Epidemiol. 2013 Jun;66(6):648-53). Therefore it is best left
to further research rather than any arbitrary statements. With that
said, ...

On Sun, Sep 15, 2013 at 12:35 PM, Steve Samuels <sjsamuels@gmail.com> wrote:
> Nicole:
> I didn't see that nuance. It changes the picture, because it means that you do not have a competing risks problem. For risks to be competing, only one can be observed  (Kalbfleisch & Prentice, 2002, p. 248). In your case, infection and death can both be observed. The proper analysis, therefore, is a standard, single-response, survival model.

This is not correct, and there is a number of detailed analyses of BMT
and other data that discuss it in detail. The competing risk problem
still exists and the CIF / Fine-Gray approach is appropriate, as long
as it is the infection that is the endpoint of interest. In this
scenario, only one event can be observed, because patients are
censored at the time of infection and the data on their further deaths
become irrelevant for the model (and, in fact, invisible through
-stset- assignment). There is an obvious informative censoring issue
because of the asymetry: no infections after death. Using a net
survival estimator would overestimate the risk of infection, which may
introduce minimal or major bias depending on the risk of each event in
the population. The same reasoning is widely applied in other
scenarios of cancer epidemiology, where cancer recurrence and death
(of any cause) are treated as competing events, even though people
obviously die after cancer recurrence (yet they do not recur after
death). Again, there is a big conceptual difference between modeling
biological effect of covariate on one type of event versus predicting
real-life outcomes.
AO

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