predict: Predicts according to the fitted SurvCART or LongCART tree

In LongCART: Recursive Partitioning for Longitudinal Data and Right Censored Data Using Baseline Covariates

Predicts according to the fitted SurvCART or LongCART tree

Description

Predicts according to the fitted SurvCART or LongCART tree.

Usage

## S3 method for class 'SurvCART'
predict(object, newdata, ...)
## S3 method for class '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.

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.

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

Author(s)

Madan Gopal Kundu [e-mail]

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

#--- 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)

LongCART documentation built on May 18, 2022, 1:06 a.m.