A Knowledge Base of Designs and Statistical Methods for Adaptive Clinical Dose-Finding Trials

Abstract

Adaptive clinical dose-finding trials aim to identify an optimal drug dose for use in subsequent phase II and III trials. In adaptive dose-finding trials, dose levels for newly included patients are informed by outcomes of patients that received the drug earlier in the trial. The body of methodological research on adaptive dose-finding trials is extensive, but a clear overview is lacking. The goal of this paper is to provide a knowledge base of designs and statistical methods for adaptive clinical dose-finding trials by means of a literature review. We identified 315 adaptive dose-finding trial methodology articles of which the majority was inspired by oncology. Recent methods focused on identification of an optimal dose considering both toxicity and efficacy endpoints, and addressing challenges related to subgroup-specific dose-finding, dose-finding for combination therapies, and incorporation of delayed outcomes in dose-finding. These developments are driven by the emergence of newer classes of cancer drugs, such as targeted therapies and immunotherapies, and by initiatives like Project Optimus. Most articles focused on model-based designs, like the Continual Reassessment Method (CRM), but recent years have seen a strong increase in model-assisted or interval-based designs, including Toxicity Probability Interval (TPI) and Bayesian Optimal Interval (BOIN) designs and expansions thereof. Considering the increasing availability and large variety of adaptive dose-finding trial designs, it is challenging for researchers to find relevant designs tailored to their needs. Therefore, we provide an interactive map summarizing the classification of our results to facilitate the identification of relevant designs/methods, which we will update regularly.