Personalized Risk-Based Screening Design for Comparative Two-Arm Group Sequential Clinical Trials.

Yeonhee Park

doi:10.3390/jpm12030448

Abstract

Personalized medicine has been emerging to take into account individual variability in genes and environment. In the era of personalized medicine, it is critical to incorporate the patients’ characteristics and improve the clinical benefit for patients. The patients’ characteristics are incorporated in adaptive randomization to identify patients who are expected to get more benefit from the treatment and optimize the treatment allocation. However, it is challenging to control potential selection bias from using observed efficacy data and the effect of prognostic covariates in adaptive randomization. This paper proposes a personalized risk-based screening design using Bayesian covariate-adjusted response-adaptive randomization that compares the experimental screening method to a standard screening method based on indicators of having a disease. Personalized risk-based allocation probability is built for adaptive randomization, and Bayesian adaptive decision rules are calibrated to preserve error rates. A simulation study shows that the proposed design controls error rates and yields a much smaller number of failures and a larger number of patients allocated to a better intervention compared to existing randomized controlled trial designs. Therefore, the proposed design performs well for randomized controlled clinical trials under personalized medicine.

Highlights

We considered four group sequential clinical trial designs: traditional randomization with 1:1 (Trad), response-adaptive randomization without incorporating covariates (RAR), and covariate-adjusted response-adaptive randomization using (2) and (3)
We called the proposed design BaCARA, which used the personalized allocation probability (2) to randomize the patients and monitor the treatment effect based on the proposed group sequential test statistic (5) through the Bayesian sequential monitoring rule
To compare the results with Trad, RAR, CARA1, and CARA2, we included the results of BaCARA in the last column of Tables 2–4

Summary

Introduction

In the era of personalized medicine, molecularly targeted agents have been developed for disease treatment and prevention, e.g., trastuzumab [1,2], crizotinib [3,4], and erlotinib [5,6]. Novel statistical methods and clinical trial designs have been proposed for the novel targeted therapy. Park [7] reviewed statistical methods evaluating the effect of the targeted therapy with a certain genetic mutation on multiple disease types. Biomarker-based clinical trial designs have been proposed to address the one-size-fits-all issue [8–11]. Adaptive enrichment designs propose the enrichment rule to identify the patients who are expected to get more benefit from the experimental treatment and restrict the enrollment adaptively to the treatment sensitive patients [12,13]. We are interested in how personalized medicine works on randomization of treatments for clinical trials. The fixed randomization makes simple to execute the clinical trials.

Methods

Results

Conclusion