[R] How do I specify a partially completed survival analysis model?

[David Winsemius]

On Nov 20, 2009, at 11:07 AM, RWilliam wrote:

>
> In reply to suggestion by David W., setting an offset parameter  
> doesn't seem
> to work as R is not recognizing the "X2" part of  coxph(
> Surv(Time,Censor)~X1, offset=log(4.3*X2), data= a ). Also, here's some
> sample data:
>

The problem, arising as a result of not having a dataset against which  
to test my memories of syntactic niceties, is that glm and coxph use  
different methods of supplying offsets. Thereau and Gramsch's book has  
examples, but if you did not have the book you still had alternatives.  
A bit of searching with the terms: coxph Therneau offset;  produced  
lots of hits for the occurrence of offset in warning messages so  
adding -warning to that search then produced a hit to the Google books  
look at T&G's text with a worked example:

 > a$logX2 <- log(a$X2)
 > coxph(Surv(Time,Censor)~X1 + offset(logX2), data= a )
Call:
coxph(formula = Surv(Time, Censor) ~ X1 + offset(logX2), data = a)


      coef exp(coef) se(coef)     z    p
X1 -0.885     0.413     1.43 -0.62 0.54

#Or just:

 > coxph(Surv(Time,Censor)~X1 + offset(log(4.3*X2)), data= a )



>   X1         X2         Time            Censor
> 1   1 0.40619454  77.00666      0
> 2   1 0.20717868 100.00000      0
> 3   1 0.77360963  79.03463      1
> 4   1 0.62221954 100.00000      0
> 5   1 0.32191280 100.00000      0
> 6   1 0.73790704  72.84842      0
> 7   1 0.65012237 100.00000      0
> 8   1 0.71596105 100.00000      0
> 9   1 0.74787202  84.00172      0
> 10  1 0.66803790  41.65760      0
> 11  1 0.79922364  92.41999      0
> 12  1 0.76433736  90.99983      0
> 13  1 0.57014524 100.00000      0
> 14  1 0.39642235 100.00000      0
> 15  1 0.55756045 100.00000      0
> 16  0 0.60079340 100.00000      0
> 17  0 0.43630695 100.00000      0
> 18  0 0.09388013 100.00000      0
> 19  0 0.55956791 100.00000      0
> 20  0 0.52491597  97.71884      1
>
> where we set the coefficient of X2 to be 8.
>
>
>
> RWilliam wrote:
>>
>> Sorry for being impatient but is there really no way of doing this  
>> at all?
>> It's quite urgent so any help is very much appreciated. Thank you.
>>
>>
>>
>> RWilliam wrote:
>>>
>>> Hello,
>>>
>>> I just started using R to do epidemiologic simulation research  
>>> using the
>>> Cox proportional hazard model. I have 2 covariates X1 and X2 which  
>>> I want
>>> to model as h(t,X)=h0(t)*exp(b1*X1+b2*X2). I assume independence  
>>> of X
>>> from t.
>>>
>>> After I simulate Time and Censor data vectors denoting the  
>>> censoring time
>>> and status respectively, I can call the following function to fit  
>>> the
>>> data into the Cox model (a is a data.frame containing 4 columns  
>>> X1, X2,
>>> Time and Censor):
>>> b = coxph (Surv (Time, Censor) ~ X1 + X2, data = a, method =  
>>> "breslow");
>>>
>>> Now the purpose of me doing simulation is that I have another  
>>> mechanism
>>> to generate the number b2. From the given b2 (say it's 4.3), Cox  
>>> model
>>> can be fit to generate b1 and check how feasible the new model is.  
>>> Thus,
>>> my question is, how do I specify such a model that is partially  
>>> completed
>>> (as in b2 is known). I tried things like  
>>> Surv(Time,Censor)~X1+4.3*X2, but
>>> it's not working. Thanks very much.
>>>
>>
>>
>
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>
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT