Improving early phase oncology clinical trial design: A case study

Abstract

This short communication presents a first in human Bayesian Optimal Interval design case study. The study design and associated operating characteristics are discussed, together with study amendments proposed whilst the study was ongoing. Simulations investigating the impact of the amendments on the operating characteristics of the study design are presented. Lessons learnt from the case study, including providing practical advice when designing smarter early phase oncology trials to identify the maximum tolerate dose are also summarised. It is argued that model‐assisted designs are simple to implement, flexible and perform significantly better than the commonly used “3 + 3” design, and thus should become the go to design for statisticians when limited information is known about the dose toxicity curve.

Improving early phase oncology clinical trial design: An opportunity for statisticians

“…However, since 2012 there has not been any rapid uptake in model‐based or model‐assisted designs, despite published evidence that such designs are more efficient in identifying the MTD. Phillips and Clark 2 proposal that model‐assisted designs should be the go‐to design for statisticians is timely. Statisticians have an opportunity to shape and improve early phase oncology drug development programs by introducing newer, more efficient model‐based and model‐assisted designs when estimating the MTD.…”

“…Where limited information is available about the dose–response model, model‐assisted designs are preferred. Phillips and Clark 2 argue that newer Bayesian model‐assisted designs, which are readily available via existing shiny apps should become the go to design for statisticians for estimating the MTD. This manuscript assesses how much progress has been made during the past 15 years in rolling out more efficient model‐based and model‐assisted designs in early phase oncology.…”

Improving early phase oncology clinical trial design: The case for finding the optimal biological dose

“…Bayesian model‐based and model‐assisted designs have been used in recent years to improve the estimation of the Maximum Tolerated Dose (MTD) in First in Human early phase oncology studies. Phillips and Clark 1 present a case study that supports the use of the model‐assisted Bayesian Optimal Interval Design (BOIN) 2 . They argue it is easy to use in practice since the escalation rules are intuitive, and the design provides flexibility with respect to a variety of different design features including cohort sizes.…”