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Continual reassessment method: a likelihood approach

J O'Quigley1, L Z Shen

  • 1Unité 436 INSERM, Paris, France.

Biometrics
|June 1, 1996
PubMed
Summary
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This study adapts the Bayesian continual reassessment method (CRM) to a likelihood framework, maintaining core features for dose-finding in clinical trials. Simulations show similar performance for final dose recommendations but highlight differences in patient allocation during trials.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • The Continual Reassessment Method (CRM) traditionally employs a Bayesian framework for dose-finding in clinical trials.
  • Bayesian methodology facilitates natural initial experimentation and sequential updating of dose levels.
  • This approach is widely used but can be computationally intensive and requires prior specification.

Purpose of the Study:

  • To adapt the Continual Reassessment Method (CRM) from a Bayesian framework to a likelihood-based approach.
  • To compare the performance of Bayesian and likelihood CRM, particularly in terms of patient dose allocation and final dose recommendations.
  • To propose practical implementation strategies for a likelihood-based CRM.

Main Methods:

  • Theoretical adaptation of the CRM from Bayesian principles to likelihood theory.

Related Experiment Videos

  • Comparative analysis of Bayesian and likelihood approaches using simulations.
  • Evaluation of performance metrics including final dose level recommendation and in-trial patient allocation.
  • Consideration of initial implementation challenges for the likelihood approach.

    • Main Results:

    • The essential features and large-sample properties of the CRM remain consistent between Bayesian and likelihood frameworks, except with degenerate priors.
    • Simulations indicate comparable performance for final recommended dose levels between the two approaches, especially for small sample sizes.
    • Significant differences emerge in the in-trial allocation of dose levels to patients, favoring the Bayesian approach in certain aspects.
    • The likelihood approach requires an initial phase (e.g., using Up-and-Down or standard CRM) until toxicity is observed to initiate the likelihood equation.

    Conclusions:

    • A likelihood-based framework offers a viable alternative to the Bayesian approach for the Continual Reassessment Method.
    • While final dose recommendations are similar, the practical implementation, particularly patient dose allocation during trials, differs.
    • A hybrid approach, starting with a simpler scheme and transitioning to the likelihood CRM upon observing toxicity, is proposed for practical implementation.