Please take this discussion offlist. It is **not** about R.
-- Bert
On Thu, Apr 25, 2013 at 5:59 AM, Paul Miller <pjmiller...@yahoo.com> wrote:
> Hi Dr. Therneau,
>
> Thanks for your reply to my question. I'm aware that many on the list do not
> like type III SS. I'm not particularly attached to the idea of using them but
> often produce output for others who see value in type III SS.
>
> You mention the problems with type III SS when testing interactions. I don't
> think we'll be doing that here though. So my type III SS could just as easily
> be called type II SS I think. If the SS I'm calculating are essentially type
> II SS, is that still problematic for a Cox model?
>
> People using type III SS generally want a measure of whether or not a
> variable is contributing something to their model or if it could just as
> easily be discarded. Is there a better way of addressing this question than
> by using type III (or perhaps type II) SS?
>
> A series of model comparisons using a LRT might be the answer. If it is, is
> there an efficient way of implementing this approach when there are many
> predictors? Another approach might be to run models through step or stepAIC
> in order to determine which predictors are useful and to discard the rest. Is
> that likely to be any good?
>
> Thanks,
>
> Paul
>
> --- On Wed, 4/24/13, Terry Therneau <thern...@mayo.edu> wrote:
>
>> From: Terry Therneau <thern...@mayo.edu>
>> Subject: Re: Trouble Computing Type III SS in a Cox Regression
>> To: r-help@r-project.org, "Paul Miller" <pjmiller...@yahoo.com>
>> Received: Wednesday, April 24, 2013, 5:55 PM
>> 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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--
Bert Gunter
Genentech Nonclinical Biostatistics
Internal Contact Info:
Phone: 467-7374
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