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A simple Bayesian decision-theoretic design for dose-finding trials.

Shenghua Kelly Fan¹, Ying Lu, You-Gan Wang

¹Department of Statistics and Biostatistics, California State University at East Bay, Hayward, CA 94542, USA. kelly.fan@csueastbay.edu

Statistics in Medicine

|July 6, 2012

Summary

This summary is machine-generated.

This study introduces a flexible Bayesian design for dose-finding trials, simplifying computations with analytic posterior distributions. The one-step-look-ahead rule proved more efficient than complex alternatives in simulations.

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Area of Science:

Biostatistics
Clinical Trial Design
Pharmacometrics

Background:

Dose-finding trials are crucial for determining optimal drug dosages.
Traditional methods can be computationally intensive and lack flexibility.
Bayesian decision-theoretic approaches offer a promising alternative.

Purpose of the Study:

To propose a flexible and computationally efficient Bayesian decision-theoretic design for dose-finding trials.
To evaluate the performance of different dose selection rules within this framework.
To compare the proposed method against existing Bayesian approaches.

Main Methods:

Developed a Bayesian decision-theoretic framework using a working model with conjugate priors.
Implemented analytic posterior distributions for computational efficiency.
Employed utility functions and dose selection rules for patient allocation.
Evaluated one-step-look-ahead (OSLA) and two-step-look-ahead rules via extensive simulations.

Main Results:

The proposed Bayesian design is flexible and computationally tractable.
The one-step-look-ahead (OSLA) dose selection rule demonstrated superior efficiency compared to the two-step-look-ahead rule under the proposed Bayesian structure.
The Bayesian method outperformed several popular existing Bayesian methods.
The impact of prior misspecification was manageable.

Conclusions:

The proposed simple and flexible Bayesian design offers an efficient approach to dose-finding trials.
OSLA is a practical and effective dose selection rule within this Bayesian framework.
This method provides a robust alternative to existing Bayesian dose-finding strategies.