LongCART (version 3.2)

predict: Predicts according to the fitted SurvCART or LongCART tree

Description

Predicts according to the fitted SurvCART or LongCART tree.

Usage

# S3 method for SurvCART
predict(object, newdata, ...)
# S3 method for LongCART
predict(object, newdata, patid, ...)

Arguments

object

a fitted object of class "SurvCART", containing a survival tree, or class "LongCART", containing a longitudinal tree.

newdata

The dataset for prediction.

patid

Variable name containing patient id in the new dataset. Must for prediction based on LongCART object

...

Please disregard.

Value

For prediction based on "SurvCART" algorithm, the dataset adds to the following variables in the new dataset:

node

Terminal node id the observation belongs to

median.T

Median event time of the terminal node id the observation belongs to

median.C

Median censoring time of the terminal node id the observation belongs to

Q1.T

First quartile for event time of the terminal node id the observation belongs to

Q1.C

First quartile for censoring time of the terminal node id the observation belongs to

Q3.T

Third quartile for event time of the terminal node id the observation belongs to

Q3.C

Third quartile for censoring time of the terminal node id the observation belongs to

For prediction based on LongCART algorithm, the dataset adds to the following variables in the new dataset:

node.id

Terminal node id the observation belongs to

profile

The fitted profile of the terminal node id the observation belongs to

predval

predicted value based on the fitted profile profile

Details

For prediction based on "SurvCART" algorithm, the predicted dataset includes the terminal node id the observation belongs to, and the median event and censoring times of the terminal id.

For prediction based on "LongCART" algorithm, the predicted dataset includes the terminal node id the observation belongs to, the fitted profile, and the predicted value based on the fitted profile. Note that the predicted value does not consider the random effects.

References

Kundu, M. G., and Harezlak, J. (2019). Regression trees for longitudinal data with baseline covariates. Biostatistics & Epidemiology, 3(1):1-22.

Kundu, M. G., and Ghosh, S. (2021). Survival trees based on heterogeneity in time-to-event and censoring distributions using parameter instability test. Statistical Analysis and Data Mining: The ASA Data Science Journal, 14(5), 466-483.

See Also

SurvCART, LongCART

Examples

# NOT RUN {
#--- LongCART example

data(ACTG175)
gvars=c("gender", "wtkg", "hemo", "homo", "drugs",
        "karnof", "oprior", "z30", "zprior", "race",
        "str2", "symptom", "treat", "offtrt")
tgvars=c(0, 1, 0, 0, 0,
         1, 0, 0, 0, 0,
         0, 0, 0, 0)
out1<- LongCART(data=ACTG175, patid="pidnum", fixed=cd4~time,
                gvars=gvars, tgvars=tgvars, alpha=0.05,
                minsplit=100, minbucket=50, coef.digits=2)
pred1<- predict.LongCART(object=out1, newdata=ACTG175, patid="pidnum")
head(pred1)

#--- SurvCART example

data(GBSG2)
GBSG2$horTh1<- as.numeric(GBSG2$horTh)
GBSG2$tgrade1<- as.numeric(GBSG2$tgrade)
GBSG2$menostat1<- as.numeric(GBSG2$menostat)

GBSG2$subjid<- 1:nrow(GBSG2)

fit<- SurvCART(data=GBSG2, patid="subjid", censorvar="cens", timevar="time", 
        gvars=c('horTh1', 'age', 'menostat1', 'tsize', 'tgrade1', 'pnodes', 'progrec', 'estrec'),  
        tgvars=c(0,1,0,1,0,1, 1,1),          
        event.ind=1,  alpha=0.05, minsplit=80, minbucket=40, print=TRUE)

pred2<- predict.SurvCART(object=fit, newdata=GBSG2)
head(pred2)
# }