[R] Binary outcome with non-absorbing outcome state
tlumley at u.washington.edu
Fri Jul 29 18:20:35 CEST 2005
On Fri, 29 Jul 2005, John Sorkin wrote:
> I am trying to model data in which subjects are followed through time to
> determine if they fall, or do not fall. Some of the subjects fall once,
> some fall several times. Follow-up time varies from subject to subject.
> I know how to model time to the first fall (e.g. Cox Proportional
> Hazards, Kaplan-Meir analyses, etc.) but I am not sure how I can model
> the data if I include the data for those subjects who fall more than
Various people have already given references that deal with marginal Cox
models. I'd second Frank Harrell's recommendation of Therneau & Grambsch.
Computationally this is very straightforward: each person has multiple
records corresponding to the times between events, and in a Cox model you
to the model formula to get the right standard errors, where id is unique
identifier for individuals.
The difficult part is deciding which person-time to compare: eg should
someone who has recently had a second event at time 500 be compared to
other people who have recently had a second event, other people who have
recently had any sort of event, other people at time 500, etc.
Another possibility is frailty models, the analogue of generalized linear
mixed models. As with GLMMs, even fitting these is tricky and statistical
theory isn't that well-developed. The survival package does have an
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