Re: [R] Binary outcome with non-absorbing outcome state

From: Thomas Lumley <tlumley_at_u.washington.edu>
Date: Sat 30 Jul 2005 - 02:20:35 EST

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
> once.

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 add

    +cluster(id)
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 implementation, though.

         -thomas



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