quint (version 2.1.0)

quint.bootstrapCI: Bootstrap method to compute confidence intervals for Qualitative Interaction Trees (Quint)

Description

A bootstrap algorithm based on Loh et al. (2015) to estimate the confidence intervals of the difference in mean outcome between the two treatments in each leaf.

Usage

quint.bootstrapCI(tree, n_boot, boot_r = 1)

Arguments

tree

a (pruned) quint object of class quint.

n_boot

number of bootstrap samples.

boot_r

bootstrap sample size expressed as proportion of total sample size. Default value is 1.

Value

Returns two lists: A first one ($tree) containing an object of the class quint, and a list ($bootinfo) with estimates obtained from the bootstrap procedure containing the following components:

nleaves

vector containing the number of leaves in each of the estimated trees in the bootstrap samples.

meanT_1

a matrix containing for each bootstrap sample (= rows) the mean outcome for Treatment A (T=1) in each leaf of the input quint tree (= columns) using the subjects in the intersection.

meanT_2

a matrix containing for each bootstrap sample (= rows) the mean outcome for Treatment B (T=2) in each leaf of the input quint tree (= columns) using the subjects in the intersection.a matrix containing the mean outcome for Treatment 2 in each leaf using the subjects in the intersection.

meandif

a matrix containing the difference in means between Treatment A and Treatment B in each leaf for each bootstrap sample.

bias_est

vector containing the bias in each leaf of the quint tree.

meanboot

vector containing the bootstrap estimates of the difference of means between treatments in each leaf.

CIs

vector containing the confidence intervals of the estimate of the difference of means between treatments in each leaf.

se_est

vector containing the new estimates of the standard error of the difference of means between treatments in each leaf.

Details

The details of this validation procedure are described in "Instability of QUalitative INteraction Trees: Quantifying uncertainty in decision trees." ( https://openaccess.leidenuniv.nl/handle/1887/83059)

References

Dusseldorp E. and Van Mechelen I. (2014). Qualitative interaction trees: a tool to identify qualitative treatment-subgroup interactions. Statistics in Medicine, 33(2), 219-237. DOI: 10.1002/sim.5933. Beck C., Dusseldorp E. and Fokkema M. (2019). Instability of QUalitative INteraction Trees: Quantifying uncertainty in decision trees. (https://openaccess.leidenuniv.nl/handle/1887/83059))

See Also

quint, prune.quint, quint.control

Examples

# NOT RUN {
data(bcrp)
formula1<- I(cesdt1-cesdt3)~cond | nationality+marital+wcht1+age+
  trext+comorbid+disopt1+uncomt1+negsoct1

set.seed(10)
control1<-quint.control(maxl=5, B=2, crit="dm")
quint1<-quint(formula1, data= subset(bcrp,bcrp$cond<3),control=control1) #Grow a QUINT tree

prquint1<-prune(quint1) #Prune tree to optimal size

bootquint1<-quint.bootstrapCI(prquint1, n_boot = 5) #apply the bootstrap procedure

#the summary of the tree with the new standard errors obtained from the bootstrap procedure
summary(bootquint1$tree)

#all results of the bootstrap procedure
bootquint1$bootinfo

#plot wiht 95% confidence intervals using the new standard errors
plot(bootquint1$tree) 
# }