R help - Nov 2011 - Second-order effect in Parametric Survival Analysis

Hi experts,

http://r.789695.n4.nabble.com/file/n4034318/Parametric_survival_analysis_2nd-order_efffect.JPG
Parametric_survival_analysis_2nd-order_efffect.JPG 
As we know a normal survival regression is the equation (1)
Well, I'ld like to modify it to be 2nd-order interaction model as shown in
equation(2)

Question:
Assume a and z is two covariates.
x = dummy variable (1 or 0)
z = factors (peoples' name)
fit <- survreg(Surv(time,censor)~x*z, data=sample,
dist="exponential")

I tried to apply survreg(), while I have few questions:
1) If */survreg(Surv(time,censor)~x*z, data=sample,
dist="exponential")/*
correct?
2) If the baseline hazard is the value excluded both x and z effects?
3) How can I get the value and plot the hazard with only x effect (but
exclude effect z)

Thanks


Best,
Ryusuke


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On Nov 12, 2011, at 7:37 AM, ryusuke wrote:
> Hi experts,
>
>
http://r.789695.n4.nabble.com/file/n4034318/Parametric_survival_analysis_2nd-order_efffect.JPG
> Parametric_survival_analysis_2nd-order_efffect.JPG
> As we know a normal survival regression is the equation (1)
> Well, I'ld like to modify it to be 2nd-order interaction model as  
> shown in
> equation(2)
>
> Question:
> Assume a and z is two covariates.
> x = dummy variable (1 or 0)
> z = factors (peoples' name)
> fit <- survreg(Surv(time,censor)~x*z, data=sample,
dist="exponential")
>
> I tried to apply survreg(), while I have few questions:
> 1) If */survreg(Surv(time,censor)~x*z, data=sample,  
> dist="exponential")/*
> correct?
The formula interface for R would expand x*z to x + z + x:z (Which is  
not the formula in your Nabble-provided-jpg, but from your later   
questions is probably what you want anyway.)
> 2) If the baseline hazard is the value excluded both x and z effects?
Maybe. You won't be  "excluding" them so much as holding their
values
jointly at zero, which may or may not be the same thing.
> 3) How can I get the value and plot the hazard with only x effect (but
> exclude effect z)
You will never be able to do so. If you have an interacting variable  
in a model, there will always be an effect of that covariate on  
predictions associated with any covariate with which it is  
interacting. You should be able to display or plot the ""x- 
effects" (note the plural) that are estimated for chosen levels of z,  
however. To accomplish that you should construct an appropriate  
data.frame and offer it as the newdata argument to predict(fit) ....  
just as you would do with any properly constructed R/S regression  
package.

There is a worked example on this posting from the Master:

http://finzi.psych.upenn.edu/Rhelp10/2010-May/240458.html

-- 
David Winsemius, MD
West Hartford, CT

Thank you Dr. David.

I try to summarize it.
Assumes x and z are two covariates:
x = dummy variable (1 or 0)
z = factors (people name)

x*z = x + z + x*z
therefore this is not a 2nd-order interactions, it should be (for an
exponential survival regression):-
h(t|(X=x,Z=z)) = exp(Beta0 + XZBeta1)

#---------------------------------------------------

I believe there is no 2nd-order interactions survival regression as I
searched over www.rseek.org. While I tried to read through the codes of
survreg(), I stuck (cannot understand) at survreg6.c

survreg6.c apply C Language which involves Cholesky decomposition
multi-matrix (first-order interactions) calculation.
1) chinv2.c
2) cholesky3.c
3) chsolve2.c (only solve the equations of first-order interactions)

If someone gives some idea or suggestion on these?
Thank you.


Best,
Ryusuke


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