Re: st: survival analysis in the presence of competing risks and multi-level data

[Salah Mahmud <salah.mahmud@gmail.com>]

Thanks Phil for very helpful pointers,
I have not seen the paper by Katsahian and Boudreau. So I appreciate
the reference. This is probably the way to go. It seems that there is
a possible R implementation. So that is great. Hopefully Stata corp
will in time add Gamma frailties to the stcrreg command.

Like you, I am not sure about the L&M approach. It was never an
attractive option for me (not sure I fully understand how it accounts
for competing risks).

Best wishes,

On Sat, Mar 12, 2011 at 5:05 PM, Phil Clayton
<philclayton@internode.on.net> wrote:
> A recent article in Statistics in Medicine dealt with this issue by extending shared frailty models to the competing risks situation:
> Katsahian and Boudreau. Estimating and testing for center effects in competing risks. Stat Med (2011). doi: 10.1002/sim.4132
>
> It's pretty hot off the press and would require a lot of work to implement. I am not sure if you can use a model based on Cox regression, eg Lunn & McNeil's method, in clustered data - but if you can it would almost certainly be a lot easier than implementing the above.
> Lunn and McNeil. Applying Cox regression to competing risks. Biometrics (1995) vol. 51 (2) pp. 524-32
>
> Phil
>
> On 13/03/2011, at 4:52 AM, Salah Mahmud wrote:
>
>> Hello,
>> Just wondering if anyone is aware of reasonable approaches (preferably
>> implementable in Stata) to fit competing risks time-to-event models to
>> clustered data.
>>
>> I have a dataset where participants are clustered by family and
>> eventually by locality. The outcome is time to first hospitalization
>> due to a certain condition, but observing this outcome could be
>> precluded by death. So I would like to account for these competing
>> risks  because my predictors will likely influence all outcomes.
>> Normally I would use something like Fine & Gray models, but the data
>> are clustered at more than one level and I would like to model the
>> effects of covariates measured at different levels (and not just
>> adjust my SE for correlation, eg, using robust SE estimates). Is there
>> a way of fitting this model in Stata (eg, using gllamm)? in a
>> different package?
>>
>> Thanks,
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