Above, I posted what I find may be errors in survival:::Prentice and 
survival:::LinYing.

Today, I found (what I believe is) another discrepancy/error in 
survival:::SelfPrentice.

The variance estimator is slightly different from codes of Therneau & Li (1999).

When extracting dfbeta, 

Therneau & Li uses db <- db[subcoh == 1,] But the survival:::SelfPrentice uses 
db <- db[gp == 0,]

In other words, Therneau & Li uses the real subcohort index (including 
duplicate observations that are internally created) but survival:::SelfPrentice 
uses the dummy index.

This may very well be intentional by the authors of the program. I am just 
reporting the discrepancy between the program and the article they cite 
(Therneau & Li, 1999)

 

-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On 
Behalf Of Ryung Kim
Sent: Thursday, August 22, 2013 9:26 PM
To: r-help@r-project.org
Subject: [R] A couple of questions regarding the survival:::cch function

Dear all,

I have  a couple of questions regarding the survival:::cch function.


1)      I notice that Prentice and Self-Prentice functions are giving identical 
standard errors (not by chance but by programming design) while their beta 
estimates are different. My guess is they are both using the standard error 
form from Self and Prentice (1986). I understand that standard errors for both 
methods are asymptotically identical, but in my simulation study I need to 
distinguish between two standard errors evaluated at different beta 
coefficients. My guess is changing the option iter.max=35 in Prentice function 
to iter.max=0 should do the trick. But I wanted to hear from the experts (or 
the author of the program) on this issue.



The fact that SE's are identical can be found by the R help example codes of 
CCH.  I'm copying and pasting them.



     subcoh <- nwtco$in.subcohort

     selccoh <- with(nwtco, rel==1|subcoh==1)

     ccoh.data <- nwtco[selccoh,]

     ccoh.data$subcohort <- subcoh[selccoh]

     ## central-lab histology

     ccoh.data$histol <- factor(ccoh.data$histol,labels=c("FH","UH"))

     ## tumour stage

     ccoh.data$stage <- factor(ccoh.data$stage,labels=c("I","II","III","IV"))

     ccoh.data$age <- ccoh.data$age/12 # Age in years



    cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data, subcoh = 
~subcohort, id=~seqno, cohort.size=4028)

    cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data,  subcoh = 
~subcohort, id=~seqno, cohort.size=4028, method="SelfPren")



2)      I also notice that Lin-Ying beta estimates are quite different from 
Self-Prentice estimates. My expectation was that the beta estimates should be 
the same (or at least very close) and only the variance estimates are supposed 
to be different. This is because Lin and Ying (1993) 's state  "the estimating 
equation... reduces to the pseduolikelihood score function of Self and 
Prentice" and Therneau and Li (1999, Lifetime Data Analysis) also state that " 
[Lin and Ying's] proposed [beta] estimates ... are identical to those of Self 
and Prentice.".  Can someone shed light on why the beta estimates in 
survival:::cch are different between two methods (by design, it seems)?



This also can be seen by the data in the example codes in R help.



      subcoh <- nwtco$in.subcohort

     selccoh <- with(nwtco, rel==1|subcoh==1)

     ccoh.data <- nwtco[selccoh,]

     ccoh.data$subcohort <- subcoh[selccoh]

     ## central-lab histology

     ccoh.data$histol <- factor(ccoh.data$histol,labels=c("FH","UH"))

     ## tumour stage

     ccoh.data$stage <- factor(ccoh.data$stage,labels=c("I","II","III","IV"))

     ccoh.data$age <- ccoh.data$age/12 # Age in years



     cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data,  subcoh = 
~subcohort, id=~seqno, cohort.size=4028, method="SelfPren")

     cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data,  subcoh = 
~subcohort, id=~seqno, cohort.size=4028, method="LinYing")

Ryung Kim
Department of Epidemiology and Population Health Albert Einstein College of 
Medicine

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