riskRegression (version 1.3.7)

predict.CauseSpecificCox: Predicting absolute risk from cause-specific Cox models

Description

Apply formula to combine two or more Cox models into absolute risk (cumulative incidence function)

Usage

# S3 method for CauseSpecificCox
predict(object, newdata, times, cause,
  landmark = NA, keep.times = 1L, keep.newdata = 1L, keep.strata = 1L,
  se = FALSE, iid = FALSE, conf.level = 0.95, ...)

Arguments

object

The fitted cause specific Cox model

newdata

A data frame containing the values of the variables in the right hand side of 'coxph' for each subject.

times

Vector of times at which to return the estimated hazards/survival

cause

Identifies the cause of interest among the competing events.

landmark

the starting time for the computation of the cumulative risk

keep.times

Logical. If TRUE add the evaluation times to the output.

keep.newdata

Logical. If TRUE add the value of the covariates used to make the prediction in the output list.

keep.strata

Logical. If TRUE add the value of the strata used to make the prediction in the output list.

se

Logical. If TRUE add the standard errors corresponding to the output.

iid

Logical. If TRUE add the influence function corresponding ot the output.

conf.level

Level of confidence.

...

not used

Value

A list containing:

• absRisk: (data table) the predictions for each subject (in rows) and each time (in columns).

• absRisk.se: (data table) the standard errors of the predictions.

• (absRisk.iid): (array) the value of the influence of each subject used to fit the object (dim 3) for each subject in newdata (dim 1) and each time (dim 2).

• times: (vector) the evaluation times.

Details

Note: for Cox regression models with time varying covariates it does not make sense to use this function, because the predicted risk has to be a measurable function of the data available at the time origin.

Examples

set.seed(5)
d <- sampleData(80,outcome="comp")
nd <- sampleData(4,outcome="comp")
d$time <- round(d$time,1)
ttt <- sort(sample(x = unique(d$time), size = 10))

# coxph function
CSC.fit <- CSC(Hist(time,event)~ X3+X8,data=d, method = "breslow")
x= predict(CSC.fit,newdata=nd,times=1:10,cause=1,se=1L)
px=print(x)
px

predCSC <- predict(CSC.fit, newdata = d, cause = 2, times = ttt)
predCSC.se <- predict(CSC.fit, newdata = d[1:5,], cause = 2, times = ttt,
                      se = TRUE,keep.newdata=TRUE)
predCSC.iid <- predict(CSC.fit, newdata = d[1:5,],
                       cause = 2, times = ttt, iid = TRUE)

# predCSC.se$absRisk.se
# sqrt(apply(predCSC.iid$absRisk.iid[,1,]^2,1,function(x){sum(x)}))
## strata
CSC.fit.s <- CSC(list(Hist(time,event)~ strata(X1)+X2+X9,
 Hist(time,event)~ X2+strata(X4)+X8+X7),data=d, method = "breslow")
predict(CSC.fit.s,cause=1,times=ttt,se=1L)
# cph function
CSC.cph <- CSC(Hist(time,event)~ X1+X2,data=d, method = "breslow", fitter = "cph")

predict(CSC.cph, newdata = d, cause = 2, times = ttt)

# landmark analysis
T0 <- 1
predCSC_afterT0 <- predict(CSC.fit, newdata = d, cause = 2, times = ttt[ttt>T0], landmark = T0)
predCSC_afterT0