Designing phase II clinical trials to target subgroup of interest in a heterogeneous population: A case study using an R package. (1st September 2018)

• Record Type: Journal Article
• Authors: Cabarrou, B.; Sfumato, P.; Leconte, E.; Boher, J.M.; Filleron, T.
• Is Part Of: Computers in biology and medicine. Volume 100(2018)
• Page Start: 239
• Page End: 246
• Publication Date: 2018-09-01
• Subjects: Heterogeneity -- Phase II clinical trial -- Single-arm -- Simon two-stage design -- Stratified adaptive design -- Target subgroup -- R package; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.06.034
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Phase II trials that evaluate target therapies based on a biomarker must be well designed in order to assess anti-tumor activity as well as clinical utility of the biomarker. Classical phase II designs do not deal with this molecular heterogeneity and can lead to an erroneous conclusion in the whole population, whereas a subgroup of patients may well benefit from the new therapy. Moreover, the target population to be evaluated in a phase III trial may be incorrectly specified. Alternative approaches are proposed in the literature that make it possible to include two subgroups according to biomarker status (negative/positive) in the same study. Jones, Parashar and Tournoux et al. propose different stratified adaptive two-stage designs to identify a subgroup of interest in a heterogeneous population that could possibly benefit from the experimental treatment at the end of the first or second stage. Nevertheless, these designs are rarely used in oncology research. After introducing these stratified adaptive designs, we present an R package ( ph2hetero ) implementing these methods. A case study is provided to illustrate both the designs and the use of the R package. These stratified adaptive designs provide a useful alternative to classical two-stage designs and may also provide options in contexts other than biomarker studies.