quint.control: Control Parameters for Quint Algorithm

Description

Various parameters that control aspects of the quint algorithm. Appendix A of Dusseldorp & Van Mechelen (2013) gives a detailed overview of the choices that can be made.

Usage

quint.control(crit = "es", maxl=10, a1 = NULL, a2 = NULL, w = NULL, 
Bootstrap = TRUE, B = 25, dmin = 0.3)

Arguments

crit

the type of difference in treatment outcome used in the partitioning criterion: "es" (Treatment effect sizes) or "dm" (Difference in treatment means).

maxl

maximum number of leaves ($L$) of the tree. Default value is 10.

the minimal sample size of Treatment A ($T=1$) in a leaf.

the minimal sample size of Treatment B ($T=2$) in a leaf.

a vector with w1 and w2 representing the weights of, respectively, the Difference in treatment outcome component and the Cardinality component of the partitioning criterion. If crit = "dm", the default value of $w1$ is $1/ log(1+IQR(Y))$. If crit = "es",

Bootstrap

whether the bias-corrected bootstrap procedure should be performed. The default is TRUE.

the number of bootstrap samples to be drawn. The default is 25.

dmin

the minimum absolute standardized mean difference in treatment outcome in each of the two leaves after the first split of the tree. This value is used to check whether a qualitative interaction is present in the data (the qualitative interaction condition

Value

A list containing the options.

References

Dusseldorp E. and Van Mechelen I. (2013). Qualitative interaction trees: a tool to identify qualitative treatment-subgroup interactions. Statistics in Medicine, first online 6 Aug 2013. DOI: 10.1002/sim.5933.

Examples

Run this code

data(bcrp)
formula1<- I(cesdt1-cesdt3)~cond | nationality+marital+wcht1+age+
trext+comorbid+disopt1+uncomt1+negsoct1
#Specify the Difference in treatment outcome as Difference in means
#and skip the bias-corrected bootstrap procedure
#and change the maximum number of leaves
control3<-quint.control(crit="dm",Bootstrap=FALSE,maxl=3) 
quint3<-quint(formula1, data= subset(bcrp,cond<3),control=control3)
summary(quint3)

#Set number of bootstrap samples at 10
control4<-quint.control(B=10)

#Set minimal sample size in each treatment group at 5
control5<-quint.control(a1=5,a2=5)