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Nolan A Wages1, Mark R Conaway, John O'Quigley

  • 1Department of Mathematics and Computer Science, Hampden-Sydney College, Hampden-Sydney, Virginia 23943, USA. nwages@hsc.edu

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Summary
This summary is machine-generated.

This study introduces a new dose-finding design for oncology trials with multiple agents, relaxing the monotonicity assumption to estimate the maximum tolerated dose (MTD) even when toxicity orderings are unknown. The method effectively handles complex scenarios in combination drug trials.

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Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Current Phase I oncology trials often assume a monotonic dose-toxicity relationship, limiting their application to single agents or implicitly ordered doses.
  • Estimating the maximum tolerated dose (MTD) is crucial, but existing methods struggle when the monotonicity assumption fails, particularly in combination therapy trials.

Purpose of the Study:

  • To develop a novel dose-finding design for oncology trials involving multiple agents where the monotonicity assumption may not hold.
  • To address scenarios in Phase I trials where the precise ordering of toxicity probabilities for different treatment combinations is not known a priori.

Main Methods:

  • The proposed design accommodates partially ordered toxicity probabilities by considering all consistent orderings.
  • It integrates the Continual Reassessment Method (CRM) to efficiently estimate the MTD within these possible orders, simplifying to the standard CRM when full ordering is known.

Main Results:

  • Simulations were conducted to compare the new design's performance against the Bayesian approach to partial orders (POCRM).
  • The new design demonstrated effectiveness in estimating MTD combinations in partially ordered settings, showing promise for complex multi-drug trials.

Conclusions:

  • The developed design effectively estimates MTD combinations in dose-finding studies with partial toxicity ordering.
  • As a multivariate generalization of the CRM that relaxes monotonicity, it bridges the gap between single-agent and multiple-agent trial designs.