Subgroup identification in clinical trials: Comparing methods QUINT and OTR with a focus on subgroups with no treatment difference

In clinical trials, heterogeneity of treatment effect often exists between patients with different pretreatment characteristics, such as age, gender, weight, etc. In response to such issue, various subgroup identification approaches have been proposed. Two methods among them, Qualitative Interaction Tree (QUINT) and a method adapted from an optimal treatment regimes (OTR) approach proposed by Zhang et al. (2012), are compared in this paper. These two methods identify three types of subgroups in a situation with two treatments (A and B): one subgroup for which treatment A is better than treatment B, one for which treatment B is better than treatment A, and one for which the difference between the two treatment outcomes is negligible (called ”indifference group”). A simulation study was conducted to compare the two methods with regard to their recovery performance (quantified by type I error rates, type II error rates, Cohen’s κ agreement to the true subgroups, and splitting...Show moreIn clinical trials, heterogeneity of treatment effect often exists between patients with different pretreatment characteristics, such as age, gender, weight, etc. In response to such issue, various subgroup identification approaches have been proposed. Two methods among them, Qualitative Interaction Tree (QUINT) and a method adapted from an optimal treatment regimes (OTR) approach proposed by Zhang et al. (2012), are compared in this paper. These two methods identify three types of subgroups in a situation with two treatments (A and B): one subgroup for which treatment A is better than treatment B, one for which treatment B is better than treatment A, and one for which the difference between the two treatment outcomes is negligible (called ”indifference group”). A simulation study was conducted to compare the two methods with regard to their recovery performance (quantified by type I error rates, type II error rates, Cohen’s κ agreement to the true subgroups, and splitting performance of the derived trees) and their predictive performance (quantified using the difference between the true expected treatment outcome and the estimated treatment outcome of sample data and population data). Results of the simulation study suggested that QUINT has its advantage in recovering the subgroups, and the method adapted from the OTR approach has its advantage in predicting treatment outcome.Show less

Leiden University Student Repository

Documents

In Collections

Subgroup identification in clinical trials: Comparing methods QUINT and OTR with a focus on subgroups with no treatment difference