Description Usage Arguments Details Value Author(s) See Also Examples
Evaluation of estimated survival or event probabilities at given times and covariate constellations.
1 2 3 4 
object 
A fitted object of class "prodlim". 
times 
Vector of times at which to return the estimated probabilities (survival or absolute event risks). 
newdata 
A data frame with the same variable names as those that appear on the right hand side of the 'prodlim' formula. If there are covariates this argument is required. 
level.chaos 
Integer specifying the sorting of the output: ‘0’ sort by time and newdata; ‘1’ only by time; ‘2’ no sorting at all 
type 
Choice between "surv","risk","cuminc","list": "surv": predict survival probabilities only survival models "risk"/"cuminc": predict absolute risk, i.e., cumulative incidence function. "list": find the indices corresponding to times and newdata. See value. Defaults to "surv" for twostate models and to "risk" for competing risk models. 
mode 
Only for 
bytime 
Logical. If TRUE and 
cause 
Character (other classes are converted with 
... 
Only for compatibility reasons. 
Predicted (survival) probabilities are returned that can be plotted, summarized and used for inverse of probability of censoring weighting.
type=="surv"
A list or a matrix with survival probabilities
for all times and all newdata.
type=="risk"
or type=="cuminc"
A list or a matrix with cumulative incidences for all
times and all newdata.
type=="list"
A list with the following components:
times 
The argument 
predictors 
The relevant part of the argument 
indices 
A list with the following components

dimensions 
a list with the following
components: 
Thomas Alexander Gerds <tag@biostat.ku.dk>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  dat < SimSurv(400)
fit < prodlim(Hist(time,status)~1,data=dat)
## predict the survival probs at selected times
predict(fit,times=c(3,5,10))
## NA is returned when the time point is beyond the
## range of definition of the KaplanMeier estimator:
predict(fit,times=c(1,0,10,100,1000,10000))
## when there are strata, newdata is required
## or neighborhoods (i.e. overlapping strata)
mfit < prodlim(Hist(time,status)~X1+X2,data=dat)
predict(mfit,times=c(1,0,10,100,1000,10000),newdata=dat[18:21,])
## this can be requested in matrix form
predict(mfit,times=c(1,0,10,100,1000,10000),newdata=dat[18:21,],mode="matrix")
## and even transposed
predict(mfit,times=c(1,0,10,100,1000,10000),newdata=dat[18:21,],mode="matrix",bytime=TRUE)

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