I read the digest form which puts me behind, plus the last 2 days have been solid meetings
with an external advisory group so I missed the initial query. Three responses.
1. The clogit routine sets the data up properly and then calls a stratified Cox model. If
you want the survConcordance routine to give the same answer, it also needs to know about
the strata
survConcordance (Surv(rep(1, 76L), resp) ~ predict(fit) + strata(ID),
data=dat)
I'm not surprised that you get a very different answer with/without strata.
2. I've never thought of using a robust variance for the matched case/control model. I'm
having a hard time wrapping my head around what you would expect that to accomplish
(statistically). Subjects are already matched on someone from the same site, so where
does a per-site effect creep in? Assuming there is a good reason and I just don't see it
(not an unwarranted assumption), I'm not aware of any work on what an appropriate variance
would be for the concordance in that case.
3. I need to think about the large variance issue.
Terry Therneau
On 01/20/2016 08:09 PM, r-help-requ...@r-project.org wrote:
Hi,
I'm running conditional logistic regression with survival::clogit. I have
"1-1 case-control" data, i.e., there is 1 case and 1 control in each strata.
Model:
fit <- clogit(resp ~ x1 + x2, strata(ID), cluster(site), method ="efron",
data = dat)
Where resp is 1's and 0's, and x1 and x2 are both continuous.
Predictors are both significant. A snippet of summary(fit):
Concordance= 0.763 (se = 0.5 )
Rsquare= 0.304 (max possible= 0.5 )
Likelihood ratio test= 27.54 on 2 df, p=1.047e-06
Wald test = 17.19 on 2 df, p=0.0001853
Score (logrank) test = 17.43 on 2 df, p=0.0001644, Robust = 6.66
p=0.03574
The concordance estimate seems good but the SE is HUGE.
I get a very different estimate from the survConcordance function, which I
know says computes concordance for a "single continuous covariate", but it
runs on my model with 2 continuous covariates....
survConcordance(Surv(rep(1, 76L), resp) ~ predict(fit), dat)
n= 76
Concordance= 0.9106648 se= 0.09365047
concordant discordant tied.risk tied.time std(c-d)
1315.0000 129.0000 0.0000 703.0000 270.4626
Are both of these concordance estimates valid but providing different
information?
Is one more appropriate for measuring "performance" (in the AUC sense) of
conditional logistic models?
Is it possible that the HUGE SE estimate represents a convergence problem
(no warnings were thrown when fit the model), or is this model just useless?
Thanks!
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
