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:joecerad...@gmail.com] Sent: Tuesday, January 19, 2016 12:48 PM To: r-help@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 joecerad...@gmail.com / 914.707.8506 wyocoopunit.org [[alternative HTML version deleted]] ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues ______________________________________________ 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.
