| quint.validate {quint} | R Documentation |
Validation of a Qualitative Interaction Tree
Description
A bootstrap-based validation procedure to estimate the optimism in the effect sizes of a QUINT tree which gives insight in the generalizability of the results.
Usage
quint.validate(object, B = 10, allresults = FALSE)
Arguments
object |
a (pruned) QUINT tree object of class |
B |
number of bootstrap samples. Default number is 10; for better accuracy |
allresults |
option to return an extended list of output. Default is set to FALSE. See Value section for details. |
Details
In this procedure bootstrap trees are grown of the same leaf size as the (pruned) QUINT tree. The bootstrap samples are drawn from the data used to grow the original tree. For every bootstrap tree the largest and smallest (i.e., largest negative) treatment mean differences (or treatment effect sizes) of two leaves are saved. Treatment mean differences in the leaves are then predicted using the original data set as input for each bootstrapped tree. From these predictions, the largest and smallest treatment mean differences are saved. For each bootstrap tree, the largest predicted treatment effect is subtracted from the largest treatment effect in the bootstrap sample. The average of these values is the bias (i.e., the optimism) for the largest treatment effects. This is done likewise for the smallest treatment effects. Subsequently, the bias is computed as the difference between the bias for the largest effects minus the bias for the smallest effects.
The details of this validaton procedure are described in Appendix C of Dusseldorp & Van Mechelen (2014).
Value
Returns a list with the following components:
estopt |
the estimated optimism for either the treatment effect size (biasd) or the raw treatment mean difference (biasdif). |
li |
a data frame with leaf information output similar to the leaf information output of the (pruned) QUINT tree object. An extra column is added for the bias-corrected differences in treatment outcomes (d or diff). The bias-corrected values are only computed for the leaves with the most extreme values, i.e. the largest and smallest treatment effects. Hence, the other leaves get the value NA in this column. |
optd |
a matrix with computed estimated optimism of the treatment effect size per
bootstrapp tree. The first column contains the difference between the largest and smallest
effect size of the bootstrapped tree. The second column contains the difference between the
largest and smallest predicted effect size. Returned when |
optdif |
a matrix with computed estimated optimism of the raw mean difference bootstrapped tree.
The first column contains the difference between the largest and smallest raw mean difference of
the bootstrapped tree. The second column contains the difference between the
largest and smallest predicted raw mean difference. Returned when |
resultd |
a vector with the estimated overall mean optimism, the mean bias for the smallest
and for the largest effect size. Returned when |
resultdif |
a vector with the estimated overall mean optimism, the mean bias for the smallest and
largest raw mean difference. Returned when |
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.
See Also
quint, prune.quint, quint.control, quint.bootstrapCI
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)
quint1<-quint(formula1, data= subset(bcrp,cond<3),control=control1) #Grow a QUINT tree
prquint1<-prune(quint1) #Prune tree to optimal size
set.seed(3)
valquint1<-quint.validate(prquint1, B = 5) #estimate the optimism by bootstrapping 5 times
valquint1
## End(Not run)