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
> stageiia 1.6565 5.241 0.5193 3.190 1.4e-03
> stageiib 1.6928 5.435 0.4902 3.453 5.5e-04
> stageiii 1.8211 6.179 0.5130 3.550 3.9e-04
> stageiv 2.3251 10.227 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 144 LR chi2 117.80 R2 0.559
> Events 102 d.f. 11 Dxy -0.459
> Center 2.4016 Pr(> chi2) 0.0000 g 1.083
> Score chi2 122.57 gr 2.953
> Pr(> chi2) 0.0000
>
> Coef S.E. Wald Z Pr(>|Z|)
> dexamethasonemg -0.0863 0.0192 -4.49 <0.0001
> paincontrol=Not Epidural 0.6027 0.1203 5.01 <0.0001
> histgrade=g2 0.9340 0.2209 4.23 <0.0001
> histgrade=g3 1.2749 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=iv 2.3251 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
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