frailtypack (version 2.8.2)

epoce: Estimators of the Expected Prognostic Observed Cross-Entropy (EPOCE) for evaluating predictive accuracy of joint models.

Description

This function computes estimators of the Expected Prognostic Observed Cross-Entropy (EPOCE) for evaluating the predictive accuracy of joint models using frailtyPenal, longiPenal or trivPenal. On the same data as used for estimation of the joint model, this function computes both the Mean Prognosis Observed Loss (MPOL) and the Cross-Validated Prognosis Observed Loss (CVPOL), two estimators of EPOCE. The latter corrects the MPOL estimate for over-optimism by approximated cross-validation. On external, this function only computes MPOL.

Usage

epoce(fit, pred.times, newdata = NULL, newdata.Longi = NULL)

Arguments

fit

A jointPenal object.

pred.times

Time or vector of times to compute epoce.

newdata

Optional. In case of joint models obtained with frailtyPenal or trivPenal. For models inheriting from trivPenal class, if newdata is given, newdata.Longi must be given as well. When missin

newdata.Longi

Optional. In case of joint models obtained with longiPenal or trivPenal. For models inheriting from longiPenal, if the newdata.Longi is given, newdata must be NULL, but for mode

Value

• data
  name of the data used to compute epoce
• new.data
  a boolean which is FALSE if computation is done on the same data as for estimation, and TRUE otherwise
• pred.times
  time or vector of times used in the function
• mpol
  values of MPOL for each pred.times
• cvpol
  values of CVPOL for each pred.times
• IndivContrib
  all the contributions to the log-likelihood for each pred.times
• AtRisk
  number of subject still at risk for each pred.times

References

D. Commenges, B. Liquet, C. Proust-Lima (2012). Choice of prognostic estimators in joint models by estimating differences of expected conditional Kullback-Leibler risks. Biometrics, 68(2), 380-387.

Examples

########################################
#### EPOCE on a joint frailty model ####
########################################

data(readmission)

modJoint.gap <- frailtyPenal(Surv(t.start,t.stop,event)~ cluster(id) +
  dukes + charlson + sex + chemo + terminal(death),
  formula.terminalEvent = ~ dukes + charlson + sex + chemo ,
  data = readmission, n.knots = 8, kappa =c(2.11e+08,9.53e+11),
  recurrentAG=TRUE)

# computation on the same dataset
temps <- c(200,500,800,1100)
epoce <- epoce(modJoint.gap,temps)

print(epoce)
plot(epoce)

# computation on a new dataset
# here a sample of readmission with the first 50 subjects
s <- readmission[1:100,]
epoce <- epoce(modJoint.gap,temps,newdata=s)

print(epoce)
plot(epoce)

#################################################
#### EPOCE on a joint  model for a biomarker ####
#########   and a terminal event  ###############
#################################################

data(colorectal)
data(colorectalLongi)

# Survival data preparation - only terminal events 
colorectalSurv <- subset(colorectal, new.lesions == 0)

modLongi <- longiPenal(Surv(time0, time1, state) ~ age +
treatment + who.PS, tumor.size ~  year*treatment + age +
who.PS, colorectalSurv, data.Longi =colorectalLongi,
random = c("1", "year"),  id = "id", link = "Random-effects", 
left.censoring = -3.33, hazard = "Weibull", 
method.GH = "Pseudo-adaptive")

# computation on the same dataset
time <- c(1, 1.5, 2, 2.5)
epoce <- epoce(modLongi,time)

print(epoce)
plot(epoce)

# computation on a new dataset
# here a sample of colorectal data with the first 50 subjects
s <-  subset(colorectal, new.lesions == 0 & ids.Longi <- subset(colorectalLongi, idepoce <- epoce(modLongi, time, newdata = s, newdata.Longi = s.Longi)

print(epoce)
plot(epoce)


###################################################
#### EPOCE on a joint model for a biomarker, ######
#### recurrent events and a terminal event   ######
###################################################

data(colorectal)
data(colorectalLongi)

# (computation takes around 30 minutes)
modTriv <- model.weib.RE.gap <-trivPenal(Surv(gap.time, new.lesions) ~ cluster(id)
+ age + treatment + who.PS + prev.resection + terminal(state),
formula.terminalEvent =~ age + treatment + who.PS + prev.resection, 
tumor.size ~ year * treatment + age + who.PS, data = colorectal,
data.Longi = colorectalLongi, random = c("1", "year"), id = "id", 
link = "Random-effects", left.censoring = -3.33, recurrentAG = FALSE,
hazard = "Weibull", method.GH="Pseudo-adaptive", n.nodes=7)

# computation on the same dataset
time <- c(1, 1.5, 2, 2.5)

# (computation takes around 10 minutes)
epoce <- epoce(modTriv,time)
print(epoce)
plot(epoce)

# computation on a new dataset
# here a sample of colorectal data with the first 100 subjects
s <-  subset(colorectal,  ids.Longi <- subset(colorectalLongi, id# (computation takes around 10 minutes)
epoce <- epoce(modTriv, time, newdata = s, newdata.Longi = s.Longi)

print(epoce)
plot(epoce)


dev.off()