I tried your alternative method on the example in cch() description manual.
The example data "nwtco" has not time-dependent covariates yet. I test cch()
and coxph() on the same data. But the estimation result is different. I
don't know if I did anything wrong.

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
fit.ccSP <- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data,
subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="SelfPren")
fit2.ccP <- coxph(Surv(edrel, rel) ~ stage + histol + age +
offset(-100*subcohort)+cluster(seqno),data =ccoh.data)


> fit2.ccP
Call:
coxph(formula = Surv(edrel, rel) ~ stage + histol + age + offset(-100 *
    subcohort) + cluster(seqno), data = ccoh.data)

            coef exp(coef) se(coef) robust se      z      p
stageII  -0.1245     0.883   0.1236    0.1371 -0.908 0.3600
stageIII  0.0193     1.020   0.1252    0.1517  0.127 0.9000
stageIV   0.2997     1.350   0.1370    0.1509  1.986 0.0470
histolUH  0.3518     1.422   0.0920    0.1092  3.223 0.0013
age      -0.0281     0.972   0.0144    0.0168 -1.678 0.0930
Likelihood ratio test=34.5  on 5 df, p=1.89e-06  n= 1154


> summary(fit.ccSP)
Case-cohort analysis,x$method, SelfPrentice
 with subcohort of 668 from cohort of 4028

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

Coefficients:
          Coef    HR  (95%   CI)     p
stageII  0.736 2.088 1.491 2.925 0.000
stageIII 0.597 1.818 1.285 2.571 0.001
stageIV  1.392 4.021 2.670 6.057 0.000
histolUH 1.506 4.507 3.274 6.203 0.000
age      0.043 1.044 0.996 1.095 0.069


2008/6/12 Terry Therneau <therneau@mayo.edu>:

> -----  begin included message
> In case cohort study, we can fit proportional hazard regression model to
> case-cohort data. In R, the function is cch() in Survival package
> Now I am working on case cohort analysis with time dependent covariates
> using cch() of "Survival" R package. I wonder if cch() provide this utility
> or not?
> The cch() manual does not say if time dependent covariate is allowed
> I know coxph() in Survival package can estimate time dependent covariates.
> ------ end inclusion -----------------------------------------------
>
>  The cch function was added to the package by Breslow and Lumley, neither
> of
> which appears to be monitoring the list lately.  Since it claims to
> impliment
> the methods in Li and Therneau, and I don't know the cch code, let me
> suggest an
> alternate way to create your fit:
>  Assume that your data set has the ususal coxph variables, including
> time-dependent covariates as multiple observations per subject using
> (start,
> stop) style, along with 2 other variables
>        id = a unique identifier per subject
>        case = 0 if the subject is a member of the random subcohort
>               1 if the subject is a case (an event from outside the
> subcohort)
>
> Then
>   coxph(Surv(time1, time2, status) ~ x1 + x2+ .... + offset(-100*case) +
>             cluster(id), data=mydata)
>
> Will fit the case-cohort model.  This correctly allows for time-dependent
> covariates.  It corresponds to the "Self" method of cch.
>  Why -100?  It causes the case to have a relative weight of approx 0 in a
> particular weighted mean; exp(-100) is small enough and doesn't cause
> trouble
> for the exp function.
>
>        Terry Therneau
>
>
>

	[[alternative HTML version deleted]]

