[R] Survival::coxph (clogit), survConcordance vs. summary(fit) concordance

[Joe Ceradini]

Thanks for pointing that out, Chris. That was a thoughtless typo on my part
when I was simplifying my model for the sake of posting.

I've run a whole set of models without any problems/warning. My main
question is regarding the difference between the concordance estimate that
summary(fit) reports and the concordance estimated with survConcordance,
particularly in relation to estimating clogit model performance. Also,
whether or not I should be concerned about the giant SE estimate I get for
concordance from summary(fit). This is within the context of a 1:1
case-control study (1 case and 1 control per strata).

Corrected 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.

The rest of the code and output details should be in my original post.

Thanks.
Joe

On Wed, Jan 20, 2016 at 6:11 AM, Andrews, Chris <chrisaa at med.umich.edu>
wrote:

> I only get the digest, sorry if this has already been answered.
>
> When I run your code (after creating some data) I get a warning that
> "weights are ignored in clogit".  This is a result of miscalling the clogit
> function.  The first 2 commas should be +s.
>
> library(survival)
> nn <- 1000
> dat <- data.frame(resp = rbinom(nn, 1, 0.5), x1=rnorm(nn), x2=rnorm(nn),
> ID = rep(seq(nn/2), e=2), site = rep(seq(nn/10), e=10))
> fit <- clogit(resp ~ x1 + x2, strata(ID), cluster(site), method ="efron",
> data = dat) # warning
> fit <- clogit(resp ~ x1 + x2 + strata(ID) + cluster(site), method
> ="efron", data = dat) # no warning
> summary(fit)
>
> Chris
>
> -----Original Message-----
> From: Joe Ceradini [mailto:joeceradini at gmail.com]
> Sent: Tuesday, January 19, 2016 12:48 PM
> To: r-help at r-project.org
> Subject: [R] Survival::coxph (clogit), survConcordance vs. summary(fit)
> concordance
>
> 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
> JoeCeradini at gmail.com / 914.707.8506
> wyocoopunit.org
>
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>
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-- 
Cooperative Fish and Wildlife Research Unit
Zoology and Physiology Dept.
University of Wyoming
JoeCeradini at gmail.com / 914.707.8506
wyocoopunit.org

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