Dose Finding with Escalation with Overdose Control (EWOC) in Cancer Clinical Trials

TL;DR

This paper discusses the Bayesian EWOC method for dose finding in cancer phase I trials, emphasizing its properties, prior choices, covariate use, and real-world application to improve patient safety and dose accuracy.

Contribution

It provides a comprehensive analysis of the EWOC design, including theoretical properties, practical considerations, and application to real cancer trial data.

Findings

Omitting key toxicity predictors leads to more severe side effects.

Proper covariate inclusion improves dose assignment accuracy.

EWOC effectively balances safety and efficacy in dose selection.

Abstract

Traditionally, the major objective in phase I trials is to identify a working-dose for subsequent studies, whereas the major endpoint in phase II and III trials is treatment efficacy. The dose sought is typically referred to as the maximum tolerated dose (MTD). Several statistical methodologies have been proposed to select the MTD in cancer phase I trials. In this manuscript, we focus on a Bayesian adaptive design, known as escalation with overdose control (EWOC). Several aspects of this design are discussed, including large sample properties of the sequence of doses selected in the trial, choice of prior distributions, and use of covariates. The methodology is exemplified with real-life examples of cancer phase I trials. In particular, we show in the recently completed ABR-217620 (naptumomab estafenatox) trial that omitting an important predictor of toxicity when dose assignments to cancer patients are determined results in a high percent of patients experiencing severe side effects and a significant proportion treated at sub-optimal doses.