A Novel Method of Subgroup Identification by Combining Virtual Twins with GUIDE (VG) for Development of Precision Medicines

TL;DR

This paper introduces the VG method, combining Virtual Twins and GUIDE, to improve subgroup identification for precision medicine by reducing bias and increasing statistical power in treatment effect analysis.

Contribution

The VG method innovatively integrates Virtual Twins and GUIDE to enhance subgroup detection accuracy in clinical trial data analysis.

Findings

VG reduces variable selection bias compared to Virtual Twins.

VG achieves higher statistical power than GUIDE Interaction.

Retrospective analysis demonstrates VG's practical utility.

Abstract

A lack of understanding of human biology creates a hurdle for the development of precision medicines. To overcome this hurdle we need to better understand the potential synergy between a given investigational treatment (vs. placebo or active control) and various demographic or genetic factors, disease history and severity, etc., with the goal of identifying those patients at increased risk of exhibiting clinically meaningful treatment benefit. For this reason, we propose the VG method, which combines the idea of an individual treatment effect (ITE) from Virtual Twins (Foster, et al., 2011) with the unbiased variable selection and cutoff value determination algorithm from GUIDE (Loh, et al., 2015). Simulation results show the VG method has less variable selection bias than Virtual Twins and higher statistical power than GUIDE Interaction in the presence of prognostic variables with strong treatment effects. Type I error and predictive performance of Virtual Twins, GUIDE and VG are compared through the use of simulation studies. Results obtained after retrospectively applying VG to data from a clinical trial also are discussed.