[R] nesting in CoxPH with survival package

[Katie Anweiler]

Thank you for the suggestions.

Just to clarify, my first question was more on what actual coding I
should be using to indicate a nested variable when using the coxph()
function.  I asked this after consulting several times with a local
statistician, but unfortunately neither of us are very familiar with
R.

After further consultation, I have changed the design to a 2*2 design
(2 levels of ExpTemp and Stability each) with blocking (Period).  I am
still getting the "x matrix deemed to be singular" error.

> LOEmod3alt=coxph(LOE.fit~ExpTemp+Stability+Period,data=goodexp)
Warning message:
In coxph(LOE.fit ~ ExpTemp + Stability + Period, data = goodexp) :
  X matrix deemed to be singular; variable 5
> summary(LOEmod3alt)
Call:
coxph(formula = LOE.fit ~ ExpTemp + Stability + Period, data = goodexp)

  n= 184, number of events= 105

                    coef exp(coef) se(coef)      z Pr(>|z|)
ExpTemp         -3.17825   0.04166  0.53105 -5.985 2.17e-09 ***
StabilityStatic -0.84129   0.43115  0.20470 -4.110 3.96e-05 ***
PeriodB          1.06794   2.90937  0.22859  4.672 2.98e-06 ***
PeriodC          1.23853   3.45054  0.58457  2.119   0.0341 *
PeriodD               NA        NA  0.00000     NA       NA
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
ExpTemp           0.04166    24.0047   0.01471     0.118
StabilityStatic   0.43115     2.3194   0.28866     0.644
PeriodB           2.90937     0.3437   1.85877     4.554
PeriodC           3.45054     0.2898   1.09723    10.851
PeriodD                NA         NA        NA        NA

Concordance= 0.833  (se = 0.03 )
Rsquare= 0.591   (max possible= 0.995 )
Likelihood ratio test= 164.4  on 4 df,   p=0
Wald test            = 111.1  on 4 df,   p=0
Score (logrank) test = 179.9  on 4 df,   p=0

> with(redo, table(LOEStatusfull, Period,ExpTemp))
, , ExpTemp = FIVE

             Period
LOEStatusfull  A  B  C  D
                  0 42  0 35  0
                  1  4   0 11  0

, , ExpTemp = FOUR

             Period
LOEStatusfull  A  B  C  D
                  0  0  0   0  2
                  1  0 46  0 44

As best as I can tell, none of my variables are collinear.  Are there
any other suggestions of how to deal with this error, or any more
information I can provide to help understand why I would be getting
this?

Thank you for your time and your help,

Katie

On Sat, Jan 12, 2013 at 4:54 PM, Bert Gunter <gunter.berton at gene.com> wrote:
> Katie:
>
> You need to get local statistical help. What you are doing makes no
> sense. See inline below.
>
> -- Bert
>
> On Sat, Jan 12, 2013 at 1:03 PM, David Winsemius <dwinsemius at comcast.net> wrote:
>>
>> On Jan 11, 2013, at 5:35 PM, Katie Anweiler wrote:
>>
>>> Hello all,
>>> I am trying to understand how to specify nested factors when using
>>> coxph(), and if it is appropriate to nest these factors in my
>>> situation.
>>> In the simplest form, I am testing two different temperatures, with
>>> each temperature being performed twice in different experimental
>>> periods (e.g. Temp5 performed in Period A and C, Temp4 performed in
>>> Period B and D)
>
> Period is confounded with temperature. That is the source of the
> singularity. in the message received below.
>
> You can estimate the C-A and the D-B differences.
> As I said, get statistical help. These are not R questions.
>
> -- Bert
>
>
>>> I am trying to see if survival time is affected by the treatment
>>> temperature.  To do this I am using temperature and experimental
>>> period nested within temperature as factors.
>>>
>>>> LOEtempmod.5days=coxph(LOE.stable.5days~Temp+Temp/Period,data=goodstable)
>>>
>>> Warning message:
>>> In coxph(LOE.stable.5days ~ Temp + Temp/Period,  :
>>>  X matrix deemed to be singular; variable 2 5 6 7
>>>
>>> 1. Is this an appropriate way of nesting?
>>
>>
>> Have you looked at the coxme package?
>> http://cran.r-project.org/web/packages/coxme/index.html
>>
>>> 2. Can this error message be ignored?
>>
>>
>> Sometimes R packages correctly drop variables that are exactly collinear:
>> other times the correct solution is not clear. I would think the answer in
>> this case would be "no", but do not have a lot to go on at this point.
>>
>> --
>>
>> David Winsemius, MD
>> Alameda, CA, USA
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
>
>
> --
>
> Bert Gunter
> Genentech Nonclinical Biostatistics
>
> Internal Contact Info:
> Phone: 467-7374
> Website:
> http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm