quint.control: Control Parameters for QUINT Algorithm

In quint: Qualitative Interaction Trees

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.
a1	the minimal sample size of Treatment A (T=1) in a leaf. If NULL, a1 is set to 1/10 of the sample size of the Treatment A group (assignment is done in the function quint). The minimum value is 2.
a2	the minimal sample size of Treatment B (T=2) in a leaf. If NULL, a2 is set to 1/10 of the sample size of the Treatment B group (assignment is done in the function quint). The minimum value is 2.
w	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", the default value of w1 is 1/log(1+3). The default of w2 is 1/log(0.50N).
Bootstrap	whether the bias-corrected bootstrap procedure should be performed. The default is TRUE.
B	the number of bootstrap samples to be drawn. The default is 25. We recommend a number of bootstraps of at least 25.
dmin	the minimum absolute standardized mean difference in treatment outcome in one of the leaves assigned to P1 and one of the leaves assigned to P2 of the pruned tree. This value is used to check whether a qualitative interaction is present in the data (the qualitative interaction condition); dmin controls the balance between Type I error and Type II error. The default value of dmin is 0.30.

Value

A list containing the options.

References

Dusseldorp, E., Doove, L., & Van Mechelen, I. (2016). Quint: An R package for the identification of subgroups of clients who differ in which treatment alternative is best for them. Behavior Research Methods, 48(2), 650-663. DOI 10.3758/s13428-015-0594-z

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.

See Also

quint

Examples

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 30
control4<-quint.control(B=30)

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

quint documentation built on July 2, 2022, 1:07 a.m.