[R] Survival::coxph (clogit), survConcordance vs. summary(fit) concordance
Therneau, Terry M., Ph.D.
therneau at mayo.edu
Thu Jan 21 16:01:51 CET 2016
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
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.
On 01/20/2016 08:09 PM, r-help-request at r-project.org wrote:
> 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.
> 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
> 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
> 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?
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