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!
--
Cooperative Fish and Wildlife Research Unit
Zoology and Physiology Dept.
University of Wyoming
joecerad...@gmail.com / 914.707.8506
wyocoopunit.org
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