On Sep 8, 2010, at 6:43 PM, Min-Han Tan wrote:
Dear R-help members,
Apologies - I am posting on behalf of a colleague, who is a little
puzzled
as STATA and R seem to be yielding different survival estimates for
the same
dataset when treating a variable as ordinal. Ordered() is used to
represent
an ordinal variable) I understand that R's coxph (by default) uses
the Efron
approximation, whereas STATA uses (by default) the Breslow. but we did
compare using the same approximations. I am wondering if this is a
result of
how coxph manages an ordered factor?
Essentially, this is a survival dataset using tumor grade (1, 2, 3
and 4) as
the risk factor. This is more of an 'ordinal' variable, rather than a
continuous variable. For the same data set of 399 patients, when
treating
the vector of tumor grade as a continuous variable (range of 1 to 4),
testing the Efron and the Breslow approximations yield the same
result in
both R and STATA.
However, when Hist_Grade_4 grp is converted into an ordered factor
using
ordered(), and the same scripts are applied, rather different
results are
obtained, relative to the STATA output. This is tested across the
different
approximations, with consistent results. The comparison using Efron
approximation and ordinal data is is below.
Are you sure you want an ordered factor? In R this means you will be
creating linear, quadratic and cubic contrasts. Notice the L, Q and C
designations on the coefficients. That certainly does not look to be
comparable to what you are getting from Stata. My suggestion would be
to create an un-ordered factor in R and see whether you get results
more in line with Stata's output when applied to your data.
--
David.
Your advice is very much appreciated!
Min-Han
Apologies below for the slightly malaligned output.
STATA output
. xi:stcox i.Hist_Grade_4grp, efr
i.Hist_Grade_~p _IHist_Grad_1-4 (naturally coded; _IHist_Grad_1
omitted)
failure _d: FFR_censor
analysis time _t: FFR_month
Iteration 0: log likelihood = -1133.369
Iteration 1: log likelihood = -1129.4686
Iteration 2: log likelihood = -1129.3196
Iteration 3: log likelihood = -1129.3191
Refining estimates:
Iteration 0: log likelihood = -1129.3191
Cox regression -- Efron method for ties
No. of subjects = 399 Number of obs =
399
No. of failures = 218
Time at risk = 9004.484606
LR chi2(3) =
8.10
Log likelihood = -1129.3191 Prob > chi2 =
0.0440
------------------------------------------------------------------------------
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf.
Interval]
-------------
+----------------------------------------------------------------
_IHist_Gra~2 | 1.408166 .3166876 1.52 0.128 .9062001
2.188183
_IHist_Gra~3 | 1.69506 .3886792 2.30 0.021 1.081443
2.656847
_IHist_Gra~4 | 2.540278 .9997843 2.37 0.018 1.17455
5.49403
R Output using
summary ( coxph( Surv(FFR_month,FFR_censor) ~ Hist_Grade_4grp,
method=c("breslow")))
summary ( coxph( Surv(FFR_month,FFR_censor) ~ Hist_Grade_4grp,
method=c("exact")))
summary ( coxph( Surv(FFR_month,FFR_censor) ~ Hist_Grade_4grp,
method=c("efron")))
n=399 (21 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
Hist_Grade_4grp.L 0.66685 1.94809 0.26644 2.503 0.0123 *
Hist_Grade_4grp.Q 0.03113 1.03162 0.20842 0.149 0.8813
Hist_Grade_4grp.C 0.08407 1.08771 0.13233 0.635 0.5252
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
exp(coef) exp(-coef) lower .95 upper .95
Hist_Grade_4grp.L 1.948 0.5133 1.1556 3.284
Hist_Grade_4grp.Q 1.032 0.9693 0.6857 1.552
Hist_Grade_4grp.C 1.088 0.9194 0.8392 1.410
Rsquare= 0.02 (max possible= 0.997 )
Likelihood ratio test= 8.1 on 3 df, p=0.044
Wald test = 8.02 on 3 df, p=0.0455
Score (logrank) test = 8.2 on 3 df, p=0.04202
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