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A Likelihood Perspective on Dose-Finding Study Designs in Oncology.

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Summary

This study introduces generalized likelihood ratios (GLRs) to assess statistical evidence for dose transition decisions in oncology clinical trials. GLR-based designs offer a unified framework for comparing methods and improving maximum tolerated dose (MTD) estimation.

Keywords:
dose de‐escalationdose escalationdose selectiondose transitiongeneralized law of likelihoodisotonic regressionmonotonicityphase 1 trial design

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Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Dose-finding studies in oncology utilize up-and-down dose transition rules based on dose-limiting toxicity (DLT) events.
  • Evaluating statistical evidence for dose transition decisions, specifically exceeding a target DLT rate, is crucial.

Purpose of the Study:

  • Introduce generalized likelihood ratios (GLRs) to quantify statistical evidence for dose transition decisions.
  • Develop a unified framework for comparing and understanding existing interval designs for dose escalation/de-escalation.

Main Methods:

  • Application of GLRs to single-dose likelihoods, creating a GLR-based interval design with target DLT rate and GLR cut-points.
  • Extension of the GLR approach to joint likelihoods using nonparametric (e.g., isotonic regression) or parametric models.

Main Results:

  • GLR-based interval designs provide a likelihood interpretation for existing designs and a unified comparison framework.
  • Comparison of interval designs using GLRs reveals differences, motivating alternatives that reduce over-treatment while maintaining MTD estimation accuracy.
  • Isotonic GLR performance is similar to single-dose GLR; parsimonious model-based GLR can enhance MTD estimation accuracy when the model is correct.

Conclusions:

  • GLRs offer a robust statistical framework for evidence-based dose transition decisions in oncology trials.
  • GLR-based designs facilitate the development of more efficient and accurate methods for determining the maximum tolerated dose (MTD).
  • This approach enhances the comparison and understanding of various dose-finding designs, leading to improved clinical trial strategies.