Re: [R] SEs rms cph vs survey svycoxph
Ok. That's weird. If the stage= variables are indicators for levels of a factor their cph standard errors look a bit small for 102 events. Could you show what coxph() gives? It has both the model-based and robust standard errors. I've just tried an example with fairly extreme weights and svycoxph() still agrees with coxph() up to the expected factor of n/(n-1) -thomas On Tue, Feb 25, 2014 at 6:21 PM, Nathan Pace n.l.p...@utah.edu wrote: Here are the model outputs. Nathan Survey package ca.ATE.design - svydesign(ids = ~ id, weights = ~ get.weights(ca.ATE.ps, stop.method = 'ks.mean'), data = ca.dt) Independent Sampling design (with replacement) svydesign(ids = ~id, weights = ~get.weights(ca.ATE.ps, stop.method = ks.mean), data = ca.dt) ca.ATE.dexmg.svy Call: svycoxph(formula = Surv(daysfromsurgerytodeath, as.logical(deceased)) ~ dexamethasonemg + paincontrol + histgrade + adjuvant + stage + anesthetictransfusionunits, design = ca.ATE.design) coef exp(coef) se(coef) z p dexamethasonemg-0.0863 0.917 0.0339 -2.550 1.1e-02 paincontrolNot Epidural 0.6027 1.827 0.2370 2.543 1.1e-02 histgradeg2 0.9340 2.545 0.4307 2.168 3.0e-02 histgradeg3 1.2749 3.578 0.4453 2.863 4.2e-03 adjuvantyes-0.5810 0.559 0.2529 -2.298 2.2e-02 stageib-0.4394 0.644 0.6056 -0.726 4.7e-01 stageiia1.6565 5.241 0.5193 3.190 1.4e-03 stageiib1.6928 5.435 0.4902 3.453 5.5e-04 stageiii1.8211 6.179 0.5130 3.550 3.9e-04 stageiv 2.325110.227 0.6940 3.350 8.1e-04 anesthetictransfusionunits 0.1963 1.217 0.0400 4.908 9.2e-07 Likelihood ratio test= on 11 df, p= n= 144, number of events= 102 rms package ca.ATE.dexmg.rms2 Cox Proportional Hazards Model cph(formula = Surv(daysfromsurgerytodeath, as.logical(deceased)) ~ dexamethasonemg + paincontrol + histgrade + adjuvant + stage + anesthetictransfusionunits + cluster(id), data = ca.dt, weights = get.weights(ca.ATE.ps, stop.method = ks.mean), robust = T, x = T, y = T, se.fit = T, surv = T, time.inc = 30) Model Tests Discrimination Indexes Obs 144LR chi2117.80R2 0.559 Events102d.f. 11Dxy -0.459 Center 2.4016Pr( chi2) 0.g1.083 Score chi2 122.57gr 2.953 Pr( chi2) 0. CoefS.E. Wald Z Pr(|Z|) dexamethasonemg-0.0863 0.0192 -4.49 0.0001 paincontrol=Not Epidural0.6027 0.1203 5.01 0.0001 histgrade=g20.9340 0.2209 4.23 0.0001 histgrade=g31.2749 0.2612 4.88 0.0001 adjuvant=yes -0.5810 0.1741 -3.34 0.0008 stage=ib -0.4394 0.1899 -2.31 0.0207 stage=iia 1.6565 0.2097 7.90 0.0001 stage=iib 1.6928 0.1979 8.55 0.0001 stage=iii 1.8211 0.2411 7.55 0.0001 stage=iv2.3251 0.1886 12.33 0.0001 anesthetictransfusionunits 0.1964 0.0214 9.17 0.0001 From: Thomas Lumley tlum...@uw.edu Date: Tuesday, February 25, 2014 at 3:09 PM To: Nathan Leon Pace, MD, MStat n.l.p...@utah.edu Cc: r help list r-help@r-project.org Subject: Re: [R] SEs rms cph vs survey svycoxph On Tue, Feb 25, 2014 at 2:51 PM, Nathan Pace n.l.p...@utah.edu wrote: I¹ve used twang to get ATE propensity scores. I¹ve done multivariable, case weighted Cox PH models in survey using svycoxph and in rms using cph with id(cluster) set to get robust estimates. The model language is identical. The point estimates are identical, but the CIs are considerably wider with svycoxph estimates. There is a note in the svycoxph help page stating the SEs should agree closely unless the model fits poorly. The actual note on the svycoxph help page says The standard errors agree closely with survfit.coxph for independent sampling when the model fits well, but are larger when the model fits poorly. That is, the note is for the survival curve rather than the coefficients. It's still surprising that there's a big difference, but I think we need more information. -thomas -- Thomas Lumley Professor of Biostatistics University of Auckland -- Thomas Lumley Professor of Biostatistics University of Auckland [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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.
[R] SEs rms cph vs survey svycoxph
I¹ve used twang to get ATE propensity scores. I¹ve done multivariable, case weighted Cox PH models in survey using svycoxph and in rms using cph with id(cluster) set to get robust estimates. The model language is identical. The point estimates are identical, but the CIs are considerably wider with svycoxph estimates. There is a note in the svycoxph help page stating the SEs should agree closely unless the model fits poorly. Are there other reasons for the differences? Nathan __ R-help@r-project.org mailing list 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.
Re: [R] SEs rms cph vs survey svycoxph
On Tue, Feb 25, 2014 at 2:51 PM, Nathan Pace n.l.p...@utah.edu wrote: I¹ve used twang to get ATE propensity scores. I¹ve done multivariable, case weighted Cox PH models in survey using svycoxph and in rms using cph with id(cluster) set to get robust estimates. The model language is identical. The point estimates are identical, but the CIs are considerably wider with svycoxph estimates. There is a note in the svycoxph help page stating the SEs should agree closely unless the model fits poorly. The actual note on the svycoxph help page says The standard errors agree closely with survfit.coxph for independent sampling when the model fits well, but are larger when the model fits poorly. That is, the note is for the survival curve rather than the coefficients. It's still surprising that there's a big difference, but I think we need more information. -thomas -- Thomas Lumley Professor of Biostatistics University of Auckland [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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.
Re: [R] SEs rms cph vs survey svycoxph
Here are the model outputs. Nathan Survey package ca.ATE.design - svydesign(ids = ~ id, weights = ~ get.weights(ca.ATE.ps, stop.method = 'ks.mean'), data = ca.dt) Independent Sampling design (with replacement) svydesign(ids = ~id, weights = ~get.weights(ca.ATE.ps, stop.method = ks.mean), data = ca.dt) ca.ATE.dexmg.svy Call: svycoxph(formula = Surv(daysfromsurgerytodeath, as.logical(deceased)) ~ dexamethasonemg + paincontrol + histgrade + adjuvant + stage + anesthetictransfusionunits, design = ca.ATE.design) coef exp(coef) se(coef) z p dexamethasonemg-0.0863 0.917 0.0339 -2.550 1.1e-02 paincontrolNot Epidural 0.6027 1.827 0.2370 2.543 1.1e-02 histgradeg2 0.9340 2.545 0.4307 2.168 3.0e-02 histgradeg3 1.2749 3.578 0.4453 2.863 4.2e-03 adjuvantyes-0.5810 0.559 0.2529 -2.298 2.2e-02 stageib-0.4394 0.644 0.6056 -0.726 4.7e-01 stageiia1.6565 5.241 0.5193 3.190 1.4e-03 stageiib1.6928 5.435 0.4902 3.453 5.5e-04 stageiii1.8211 6.179 0.5130 3.550 3.9e-04 stageiv 2.325110.227 0.6940 3.350 8.1e-04 anesthetictransfusionunits 0.1963 1.217 0.0400 4.908 9.2e-07 Likelihood ratio test= on 11 df, p= n= 144, number of events= 102 rms package ca.ATE.dexmg.rms2 Cox Proportional Hazards Model cph(formula = Surv(daysfromsurgerytodeath, as.logical(deceased)) ~ dexamethasonemg + paincontrol + histgrade + adjuvant + stage + anesthetictransfusionunits + cluster(id), data = ca.dt, weights = get.weights(ca.ATE.ps, stop.method = ks.mean), robust = T, x = T, y = T, se.fit = T, surv = T, time.inc = 30) Model Tests Discrimination Indexes Obs 144LR chi2117.80R2 0.559 Events102d.f. 11Dxy -0.459 Center 2.4016Pr( chi2) 0.g1.083 Score chi2 122.57gr 2.953 Pr( chi2) 0. CoefS.E. Wald Z Pr(|Z|) dexamethasonemg-0.0863 0.0192 -4.49 0.0001 paincontrol=Not Epidural0.6027 0.1203 5.01 0.0001 histgrade=g20.9340 0.2209 4.23 0.0001 histgrade=g31.2749 0.2612 4.88 0.0001 adjuvant=yes -0.5810 0.1741 -3.34 0.0008 stage=ib -0.4394 0.1899 -2.31 0.0207 stage=iia 1.6565 0.2097 7.90 0.0001 stage=iib 1.6928 0.1979 8.55 0.0001 stage=iii 1.8211 0.2411 7.55 0.0001 stage=iv2.3251 0.1886 12.33 0.0001 anesthetictransfusionunits 0.1964 0.0214 9.17 0.0001 From: Thomas Lumley tlum...@uw.edu Date: Tuesday, February 25, 2014 at 3:09 PM To: Nathan Leon Pace, MD, MStat n.l.p...@utah.edu Cc: r help list r-help@r-project.org Subject: Re: [R] SEs rms cph vs survey svycoxph On Tue, Feb 25, 2014 at 2:51 PM, Nathan Pace n.l.p...@utah.edu wrote: I¹ve used twang to get ATE propensity scores. I¹ve done multivariable, case weighted Cox PH models in survey using svycoxph and in rms using cph with id(cluster) set to get robust estimates. The model language is identical. The point estimates are identical, but the CIs are considerably wider with svycoxph estimates. There is a note in the svycoxph help page stating the SEs should agree closely unless the model fits poorly. The actual note on the svycoxph help page says The standard errors agree closely with survfit.coxph for independent sampling when the model fits well, but are larger when the model fits poorly. That is, the note is for the survival curve rather than the coefficients. It's still surprising that there's a big difference, but I think we need more information. -thomas -- Thomas Lumley Professor of Biostatistics University of Auckland __ R-help@r-project.org mailing list 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.
