Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial.
quint() is now able to handle categorical variables as candidate splitting variables. The R class of these variables should be factor. The levels of the factof can be specified as numeric values or labels.
quint.validate() is a new function with which the bias (i.e. optimism) of a fitted (pruned) quint tree can be estimated. The procedure is based on bootstrapping and makes use of the treatment effects in the leaves of the tree. The function returns the mean estimated optimism for either the effect size or the raw mean difference. It also returns the leaf info data frame identical to the leaf info of the quint object used as input, with the addition of an extra column. This extra column contains the bias-corrected treatment effect values for the leaf with the largest and the smallest (i.e., largest negative) treatment effect. An extended list of output is possible.
predict.quint() is a new S3 function to make predictions with new data using a fitted quint tree object. Three types of predictions are possible, to be specified within the "type" argument of the function. If "type='class'" the treatment subgroup classes are predicted for every patient individually. If "type='matrix'" a matrix with the positions of every patient within the fitted tree is given (i.e., the leaf number and corresponding node). If "type='li'" a leaf info data frame similar to quint$li is returned with treatment effect values (d or diff) newly computed from the input data set.
prune.quint() has been adapted such that it can handle categorical variables.
plot.quint() has been adapted such that it can make and display categorical splits.
the leaf info output of a quint object (quint$li) now displays either the effect size (d) with its standard error (se) or the raw mean difference (diff) with its standard error. This depends on whether "es" or "dm" is specified as treatment effect.
the abovementioned change for the leaf info output has also been implemented in the summary.quint() output.
the split info output of a quint object (quint$si) has an extra column. The fourth column contains the numerical split points. These are also produced for nominal variables. A fiftth column has been added to display the 'true' split points, such that the levels of a categorical variable that comprise a split point are pasted after one another.
the abovementioned change for the split info output has also been implemented in the summary.quint() output. Furthermore an additional check has been implemented for quint$si to avoid conflicts with the dimension size of quint$si objects made with version 1.0.
a bug regarding a sub function within quint() has been solved. This sub function made use of the Gamma(x) function. Gamma(x) cannot handle large numbers; as a consequence a warning was given and in the leaf output the standard error values for the effect size (d) values were NA. The implemented solution regards an approximation for computing the standard error to be used when the sample sizes exceed a certain value.
quint.control() can now return an error when the criterion is misspecified (i.e., not "es" or "dm").
The documentation has been updated. Help files for the quint.validate() and predict.quint() functions have been newly added. And a recently published paper has been added to the package documentation.