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Movement Retraining using Real-time Feedback of Performance
08:16

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Published on: January 17, 2013

Model calibration in the continual reassessment method.

Shing M Lee1, Ying Kuen Cheung

  • 1Department of Biostatistics, Mailman School of Public Health, Columbia University, NY, USA. sml2114@columbia.edu

Clinical Trials (London, England)
|June 17, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for selecting initial toxicity probabilities in continual reassessment method (CRM) trials. The proposed method offers a faster, systematic approach that yields results comparable to traditional trial-and-error simulations for dose finding.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • The continual reassessment method (CRM) is a key adaptive design for estimating maximum tolerated dose in clinical trials.
  • Model sensitivity in CRM is assessed using indifference intervals, but specifying initial toxicity probabilities is often time-consuming, relying on trial-and-error simulations.
  • Existing methods for selecting initial guesses lack systematic approaches, leading to inefficiencies in dose-finding studies.

Purpose of the Study:

  • To propose a novel algorithm for selecting initial toxicity probabilities within indifference intervals for CRM.
  • To provide a systematic and less time-consuming alternative to trial-and-error methods for CRM model calibration.
  • To evaluate the performance of the proposed algorithm against traditional methods using real clinical trial data.

Main Methods:

  • An algorithm was developed to identify indifference intervals that maximize the average percentage of correct selection across various toxicity probability scenarios.
  • The proposed method systematically selects initial guesses for toxicity probabilities, aiming for efficiency and accuracy.
  • The algorithm's performance was compared to the trial-and-error method in two real CRM trials (lymphoma and stroke).

Main Results:

  • The initial guesses selected by the proposed algorithm demonstrated operating characteristics similar to those obtained through time-consuming trial-and-error calibration.
  • Average percentage of correct selection was comparable: 61.5-62.0% (lymphoma) and 62.9-64.0% (stroke) for trial-and-error vs. the proposed approach.
  • The algorithm provides a competitive alternative, simplifying the model calibration process for CRM.

Conclusions:

  • The proposed method offers a fast and systematic approach for selecting initial toxicity probabilities in CRM.
  • This systematic approach yields results competitive with traditional trial-and-error methods, reducing calibration time.
  • The method simplifies the model calibration process, making CRM more accessible and efficient for dose-finding studies.