Tests and classification methods in adaptive designs with applications

Abstract

Statistical tests for biomarker identification and classification methods for patient grouping are two important topics in adaptive designs of clinical trials related to genomic studies. In this article, we evaluate four test methods for biomarker identification in the first stage of an adaptive design: a model-based identification method, the popular two-sided t-test, the nonparametric Wilcoxon Rank-Sum test (two-sided), and the Regularized Generalized Linear Models. For patients grouping in the second stage, we examine classification methods such as Random Forest, Elastic-net Regularized Generalized Linear Models, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). Simulation studies are carried out to assess the performance of the different methods. The best identification methods are chosen based on the well-known $F_1$ score, while the best classification techniques are selected based on the area under a receiver operating characteristic curve (AUC). The chosen methods are then applied to the Adaptive Signature Design (ASD) with a real data set from breast cancer patients for the purpose of evaluating the performance of ASD in different situations.

Keywords:

• Boosting and optimization
• logistic regression
• classification trees
• genes
• sensitive and non-sensitive patients
• targeted agent

Acknowledgments

The authors would like to thank the Editor, the associate editor, and the two referees for their insightful comments and detailed suggestions that let to a much improved version of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).