R help - Aug 2013 - 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 estimates
are different. My guess is they are both using the standard error form from Self
and Prentice (1986). I see 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. But 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) state
"that [Lin and Ying's] proposed [beta] estimates ... are identical to
those of Self and Prentice...". My understanding was that the beta
estimates should be the same (or at least very close) and only the variance
estimates are supposed to be different. Can someone shed light on why the beta
estimates are different (and it seems by design) from one another?



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

	[[alternative HTML version deleted]]

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

	[[alternative HTML version deleted]]