quint (version 1.0)

quint: Qualitative Interaction Trees

Description

This is the core function of the package. It performs a subgroup analysis by QUalitative INteraction Trees (QUINT; Dusseldorp & Van Mechelen, 2013) and is suitable for data from a two-arm randomized controlled trial. Ingredients of the analysis are: one continuous outcome variable $Y$ (the effect variable), one dichotomous treatment variable $T$ (indicating two treatment conditions, A and B), and several background characteristics $X1,\dots,XJ$. These background characteristics are measured at baseline and may be continuous, ordinal, or dichotomous. They are used to identify the following subgroups (i.e., partition classes): Subgroup 1: Those patients for whom Treatment A is better than Treatment B(P1); Subgroup 2: Those for whom Treatment B is better than Treatment A(P2), and Subgroup 3: Those for whom it does not make any difference (P3).

Usage

quint(formula, data, control = NULL)

Arguments

formula

a description of the model to be fit. The format is Y ~ T | X1 + ...+ XJ, where the variable before the | represents the dichotomous treatment variable $T$ and the variables after the | are the baseline characteristics used for partitioning.

data

a dataframe containing the variables in the model.

control

a list with control parameters as returned by quint.control.

Value

• Returns an object of class quint with components:
• call
  the call that created the object.
• crit
  the partitioning criterion used to grow the tree. The default is the Effect size criterion. Use crit="dm" for the Difference in means criterion.
• control
  the control parameters used in the analysis.
• fi
  the fit information of the final tree.
• si
  the split information of the final tree.
• li
  the leaf information of the final tree.
• data
  the data used to grow the tree.
• nind
  an $N$ x $L$ matrix indicating leaf membership.
• siboot
  an $L$ x 9 x $B$ array with split information for each bootstrap sample: C_boot = value of $C$; C_compdif = value of Difference in treatment outcome component; checkdif = indicates if pooled Difference in treatment outcome component in test set (i.e., original sample) is positive, with values: 0 = yes,1 = negative in P1, 2 = negative in P2, 3 = negative in P1 and P2; C_compcard = value of Cardinality component;checkcard = indicates if value of pooled cardinality in test set is zero, with values: 0 =no,1 = zero in P1, 2 = zero in P2, 3 = zero in P1 and P2; opt = value of optimism (C_boot-C_orig).
• indexboot
  an $N$ x $B$ matrix indicating bootstrap sample membership.

Details

The method QUINT uses a sequential partitioning algorithm. The algorithm starts with a tree consisting of a single node, that is, the root node containing all patients. Next, it follows a stepwise binary splitting procedure. This procedure implies that in each step a node, a baseline characteristic, a split of that characteristic, and an assignment of the leaves of the current tree to partition classes 1, 2, and 3 (P1 to P3) are chosen that maximize the partitioning criterion. Note that this means that after each split, all leaves of the tree are re-assigned afresh to the partition classes P1, P2, and P3.

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. Zeileis A. and Croissant Y. (2010). Extended model formulas in R: Multiple parts and multiple responses. Journal of Statistical Software, 34(1), 1-13.

See Also

summary.quint, quint.control, prune.quint, bcrp

Examples

#EXAMPLE with data from the Breast Cancer Recovery Project
data(bcrp)


#Start with expliciting the model for quint
#The outcome Y is a change score between timepoint 3 and timepoint 1
#A positive Y value indicates an improvement in depression (i.e., a decrease)

formula1<- I(cesdt1-cesdt3)~cond | nationality+marital+wcht1+age+
trext+comorbid+disopt1+uncomt1+negsoct1

#Perform a quint analysis 
#The BCRP data contain 3 conditions. Quint only works now for 2 conditions.
#For the example, we disregard the control condition
#To save computation time, we also adjust the control parameters

set.seed(2)
control1<-quint.control(maxl=5,B=2)
quint1<-quint(formula1, data= subset(bcrp,cond<3),control=control1)

#Inspect the main results of the analysis:
summary(quint1)

#Inspect the assignments of the patients to the leaves of the tree
quint1$nind

#plot the tree
plot(quint1)