quint | R Documentation |
This is the core function of the package. It performs a subgroup analysis by QUalitative INteraction Trees (QUINT; Dusseldorp & Van Mechelen, 2014) 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, e.g., A and B), and several background characteristics X1,…,XJ. These background characteristics are measured at baseline and may have a numeric or ordinal measurement level (i.e., in R a numeric or integer variable) or a nominal measurement level (i.e., in R a factor). 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).
quint(formula, data, control = NULL)
formula |
a description of the model to be fit. The format is |
data |
a dataframe containing the variables in the model. The treatment variable can be
a numeric or a factor variable with two values (or levels). WARNING: The names of your
variables should not include commas. Otherwise, |
control |
a list with control parameters as returned by |
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.
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. Treatment A is denoted with T=1, and treatment B is denoted with T=2. Can display either the output for Difference in Means (crit='dm') or Cohen's d effect size (crit='es'). |
data |
the data used to grow the tree. |
orig_data |
the original data used as input. |
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. |
formula |
a description of the model to be fit. |
pruned |
a boolean indicating whether the tree has been already pruned or not. |
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.
Zeileis A. and Croissant Y. (2010). Extended model formulas in R: Multiple parts and multiple responses. Journal of Statistical Software, 34(1), 1-13.
van der Geest M. (2018). Decision Trees: Amelioration, Simulation, Application. Can be found in: https://openaccess.leidenuniv.nl/handle/1887/65935
summary.quint
, quint.control
,
prune.quint
, bcrp
, quint.bootstrapCI
#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) #The recommended number of bootstraps is 25. quint1<-quint(formula1, data= subset(bcrp,cond<3),control=control1) quint1pr<-prune(quint1) #Inspect the main results of the analysis: summary(quint1pr) #plot the tree plot(quint1pr)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.