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.pace@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 <tlumley@uw.edu>
> Date:  Tuesday, February 25, 2014 at 3:09 PM
> To:  "Nathan Leon Pace, MD, MStat" <n.l.pace@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.pace@utah.edu> wrote:
>
> Išve used twang to get ATE propensity scores.
>
> Išve 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]]


