same subject id has to be multiple in mutiple times like following format,
Multiple records per id not allowed in cch()
so it's difficult to use cch() for time dependent covariate. Maybe coxph()
is alternative, but seems difficult because coxph() and cch() return
different estimate for same data "nwtco" even without time-dependent
covariates.
id start end event
1 3 4
1 4 5
1 5 6 1
2 2 3
2 3 4
2 4 5
2 5 6 1
I use the time-dependent covariates data "Rossi" in
http://socserv.mcmaster.ca/jfox/Books/Companion/appendix-cox-regression.pdf
I rebuild new case cohort data with time dependent variable based on Rossi
data
sc<-sample(c(TRUE,FALSE,FALSE,FALSE,FALSE,FALSE), 432, replace = TRUE)
str(Rossi)
Rossi1<-cbind(Rossi,sc)
Rossi2<-cbind(seqno,Rossi1)
subcoh1 <- Rossi2$sc
selccoh1 <- with(Rossi2, arrest.time==1|subcoh1==1)
ccoh1.data <- Rossi2[selccoh1,]
ccoh1.data$subcohort <- subcoh1[selccoh1]
str(ccoh1.data)
ccoh1.data.fold <- fold(ccoh1.data, time='week',
event='arrest', cov=12:63, cov.names='employed')
str(ccoh1.data.fold)
ccoh1.data.fold$sc<-as.logical(ccoh1.data.fold$sc)
ccoh1.data.fold$subcohort<-as.logical(ccoh1.data.fold$subcohort)
fit1.allison.2 <- cch(Surv(start, stop, arrest.time) ~
fin + age + race + wexp + mar + paro + prio + employed,
data=ccoh1.data.fold,subcoh=~subcohort,id=~seqno,cohort.size=19809)
history(1000)
fit1.allison.2 <- cch(Surv(start, stop, arrest.time) ~
+ fin + age + race + wexp + mar + paro + prio + employed,
+ data=ccoh1.data.fold,subcoh=~subcohort,id=~seqno,cohort.size=19809)
Error in cch(Surv(start, stop, arrest.time) ~ fin + age + race + wexp + :
Multiple records per id not allowed
=======================================================================
2008/6/12 Jin Wang <[EMAIL PROTECTED]>:
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 <[EMAIL PROTECTED]>:
----- 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]]
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