riskRegression (version 2020.12.08)

CSC: Cause-specific Cox proportional hazard regression

Description

Interface for fitting cause-specific Cox proportional hazard regression models in competing risk.

Usage

CSC(formula, data, cause, surv.type = "hazard", fitter = "coxph", ...)

Arguments

formula

Either a single Hist formula or a list of formulas. If it is a list it must contain as many Hist formulas as there are causes when surv.type="hazard" and exactly two formulas when surv.type="survival". If it is a list the first formula is used for the cause of interest specific Cox regression and the other formula(s) either for the other cause specific Cox regression(s) or for the Cox regression of the combined event where each cause counts as event. Note that when only one formula is given the covariates enter in exactly the same way into all Cox regression analyses.

data

A data in which to fit the models.

cause

The cause of interest. Defaults to the first cause (see Details).

surv.type

Either "hazard" (the default) or "survival". If "hazard" fit cause-specific Cox regression models for all causes. If "survival" fit one cause-specific Cox regression model for the cause of interest and also a Cox regression model for event-free survival.

fitter

Routine to fit the Cox regression models. If coxph use survival::coxph else use rms::cph.

...

Arguments given to coxph.

Value

models

a list with the fitted (cause-specific) Cox regression objects

response

the event history response

eventTimes

the sorted (unique) event times

surv.type

the value of surv.type

theCause

the cause of interest. see cause

causes

the other causes

%% ...

Details

The causes and their order are determined by prodlim::getStates() applied to the Hist object.

References

B. Ozenne, A. L. Soerensen, T.H. Scheike, C.T. Torp-Pedersen, and T.A. Gerds. riskregression: Predicting the risk of an event using Cox regression models. R Journal, 9(2):440--460, 2017.

J Benichou and Mitchell H Gail. Estimates of absolute cause-specific risk in cohort studies. Biometrics, pages 813--826, 1990.

T.A. Gerds, T.H. Scheike, and P.K. Andersen. Absolute risk regression for competing risks: Interpretation, link functions, and prediction. Statistics in Medicine, 31(29):3921--3930, 2012.

See Also

coxph

Examples

# NOT RUN {
library(prodlim)
library(survival)
data(Melanoma)
## fit two cause-specific Cox models
## different formula for the two causes
fit1 <- CSC(list(Hist(time,status)~sex+age,Hist(time,status)~invasion+epicel+log(thick)),
            data=Melanoma)
print(fit1)
# }
# NOT RUN {
library(Publish)
publish(fit1)
# }
# NOT RUN {
## model hazard of all cause mortality instead of hazard of type 2
fit1a <- CSC(list(Hist(time,status)~sex+age,Hist(time,status)~invasion+epicel+log(thick)),
             data=Melanoma,
             surv.type="surv")

## the predicted probabilities are similar
plot(predictRisk(fit1,times=500,cause=1,newdata=Melanoma),
     predictRisk(fit1a,times=500,cause=1,newdata=Melanoma))

## special case where cause 2 has no covariates
fit1b <- CSC(list(Hist(time,status)~sex+age,Hist(time,status)~1),
             data=Melanoma)
print(fit1b)
predict(fit1b,cause=1,times=100,newdata=Melanoma)


## same formula for both causes
fit2 <- CSC(Hist(time,status)~invasion+epicel+age,
            data=Melanoma)
print(fit2)

## combine a cause-specific Cox regression model for cause 2
## and a Cox regression model for the event-free survival:
## different formula for cause 2 and event-free survival
fit3 <- CSC(list(Hist(time,status)~sex+invasion+epicel+age,
                 Hist(time,status)~invasion+epicel+age),
            surv.type="surv",
            data=Melanoma)
print(fit3)

## same formula for both causes
fit4 <- CSC(Hist(time,status)~invasion+epicel+age,
            data=Melanoma,
            surv.type="surv")
print(fit4)

## strata
fit5 <- CSC(Hist(time,status)~invasion+epicel+age+strata(sex),
            data=Melanoma,
            surv.type="surv")
print(fit5)

## sanity checks

cox1 <- coxph(Surv(time,status==1)~invasion+epicel+age+strata(sex),data=Melanoma)
cox2 <- coxph(Surv(time,status!=0)~invasion+epicel+age+strata(sex),data=Melanoma)
all.equal(coef(cox1),coef(fit5$models[[1]]))
all.equal(coef(cox2),coef(fit5$models[[2]]))

## predictions
##
## surv.type = "hazard": predictions for both causes can be extracted
## from the same fit
fit2 <- CSC(Hist(time,status)~invasion+epicel+age, data=Melanoma)
predict(fit2,cause=1,newdata=Melanoma[c(17,99,108),],times=c(100,1000,10000))
predictRisk(fit2,cause=1,newdata=Melanoma[c(17,99,108),],times=c(100,1000,10000))
predictRisk(fit2,cause=2,newdata=Melanoma[c(17,99,108),],times=c(100,1000,10000))
predict(fit2,cause=1,newdata=Melanoma[c(17,99,108),],times=c(100,1000,10000))
predict(fit2,cause=2,newdata=Melanoma[c(17,99,108),],times=c(100,1000,10000))

## surv.type = "surv" we need to change the cause of interest
library(survival)
fit5.2 <- CSC(Hist(time,status)~invasion+epicel+age+strata(sex),
            data=Melanoma,
            surv.type="surv",cause=2)
## now this does not work
try(predictRisk(fit5.2,cause=1,newdata=Melanoma,times=4))

## but this does
predictRisk(fit5.2,cause=2,newdata=Melanoma,times=100)
predict(fit5.2,cause=2,newdata=Melanoma,times=100)
predict(fit5.2,cause=2,newdata=Melanoma[4,],times=100)

# }