I should hope that there is trouble, since "type III" is an undefined concept for a Cox
model. Since SAS Inc fostered the cult of type III they have recently added it as an
option for phreg, but I am not able to find any hints in the phreg documentation of what
exactly they are doing when you invoke it. If you can unearth this information, then I
will be happy to tell you whether
a. using the test (whatever it is) makes any sense at all for your data set
b. if "a" is true, how to get it out of R
I use the word "cult" on purpose -- an entire generation of users who believe in the
efficacy of this incantation without having any idea what it actually does. In many
particular instances the SAS type III corresponds to a survey sampling question, i.e.,
reweight the data so that it is balanced wrt factor A and then test factor B in the new
sample. The three biggest problems with type III are that
1: the particular test has been hyped as "better" when in fact it sometimes is sensible
and sometimes not, 2: SAS implemented it as a computational algorithm which unfortunately
often works even when the underlying rationale does not hold and
3: they explain it using a notation that completely obscures the actual question. This
last leads to the nonsense phrase "test for main effects in the presence of interactions".
There is a "survey reweighted" approach for Cox models, very closely related to the work
on causal inference ("marginal structural models"), but I'd bet dollars to donuts that
this is not what SAS is doing.
(Per 2 -- type III was a particular order of operations of the sweep algorithm for linear
models, and for backwards compatability that remains the core definition even as
computational algorthims have left sweep behind. But Cox models can't be computed using
the sweep algorithm).
Terry Therneau
On 04/24/2013 12:41 PM, r-help-requ...@r-project.org wrote:
Hello All,
Am having some trouble computing Type III SS in a Cox Regression using either
drop1 or Anova from the car package. Am hoping that people will take a look to
see if they can tell what's going on.
Here is my R code:
cox3grp<- subset(survData,
Treatment %in% c("DC", "DA", "DO"),
c("PTNO", "Treatment", "PFS_CENSORED", "PFS_MONTHS", "AGE", "PS2"))
cox3grp<- droplevels(cox3grp)
str(cox3grp)
coxCV<- coxph(Surv(PFS_MONTHS, PFS_CENSORED == 1) ~ AGE + PS2, data=cox3grp, method =
"efron")
coxCV
drop1(coxCV, test="Chisq")
require(car)
Anova(coxCV, type="III")
And here are my results:
cox3grp<- subset(survData,
+ Treatment %in% c("DC", "DA", "DO"),
+ c("PTNO", "Treatment", "PFS_CENSORED", "PFS_MONTHS", "AGE",
"PS2"))
> cox3grp<- droplevels(cox3grp)
> str(cox3grp)
'data.frame': 227 obs. of 6 variables:
$ PTNO : int 1195997 104625 106646 1277507 220506 525343 789119
817160 824224 82632 ...
$ Treatment : Factor w/ 3 levels "DC","DA","DO": 1 1 1 1 1 1 1 1 1 1 ...
$ PFS_CENSORED: int 1 1 1 0 1 1 1 1 0 1 ...
$ PFS_MONTHS : num 1.12 8.16 6.08 1.35 9.54 ...
$ AGE : num 72 71 80 65 72 60 63 61 71 70 ...
$ PS2 : Ord.factor w/ 2 levels "Yes"<"No": 2 2 2 2 2 2 2 2 2 2 ...
>
> coxCV<- coxph(Surv(PFS_MONTHS, PFS_CENSORED == 1) ~ AGE + PS2, data=cox3grp, method = "efron")
> coxCV
Call:
coxph(formula = Surv(PFS_MONTHS, PFS_CENSORED == 1) ~ AGE + PS2,
data = cox3grp, method = "efron")
coef exp(coef) se(coef) z p
AGE 0.00492 1.005 0.00789 0.624 0.530
PS2.L -0.34523 0.708 0.14315 -2.412 0.016
Likelihood ratio test=5.66 on 2 df, p=0.0591 n= 227, number of events= 198
>
> drop1(coxCV, test="Chisq")
Single term deletions
Model:
Surv(PFS_MONTHS, PFS_CENSORED == 1) ~ AGE + PS2
Df AIC LRT Pr(>Chi)
<none> 1755.2
AGE 1 1753.6 0.3915 0.53151
PS2 1 1758.4 5.2364 0.02212 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> require(car)
> Anova(coxCV, type="III")
Analysis of Deviance Table (Type III tests)
LR Chisq Df Pr(>Chisq)
AGE 0.3915 1 0.53151
PS2 5.2364 1 0.02212 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
Both drop1 and Anova give me a different p-value than I get from coxph for both
my two-level ps2 variable and for age. This is not what I would expect based on
experience using SAS to conduct similar analyses. Indeed SAS consistently
produces the same p-values. Namely the ones I get from coxph.
My sense is that I'm probably misusing R in some way but I'm not sure what I'm
likely to be doing wrong. SAS prodcues Wald Chi-Square results for its type III
tests. Maybe that has something to do with it. Ideally, I'd like to get type
III values that match those from coxph. If anyone could help me understand
better, that would be greatly appreciated.
Thanks,
Paul
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