What question did this study set out to answer?

Develop an adaptable phase II cancer trial design using Bayesian predictive probability that preserves rigorous statistical properties while allowing continuous patient monitoring.

March 28, 2008Clinical Trials

A predictive probability design for phase II cancer clinical trials

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Authors

DLDiane D. LiuThe University of Texas MD Anderson Cancer Center

Discussion

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Implication

Methodology study demonstrates earlier trial termination for inefficacious cancer therapies, highlighting enhanced flexibility and reduced patient burden.

Key Points

Develop an adaptable phase II cancer trial design using Bayesian predictive probability that preserves rigorous statistical properties while allowing continuous patient monitoring.
Constructed a Bayesian predictive probability design incorporating minimax criteria to guide early trial termination.
Implemented a three-dimensional search algorithm to determine optimal design parameters for type I and type II error control.
Evaluated operating characteristics and performance under design deviations using exact computation and simulation studies.
Strictly controlled type I and type II error rates while enabling continuous outcome evaluation instead of rigid multi-stage checkpoints.
Yielded earlier termination and lower expected sample sizes under the null hypothesis when an experimental regimen proved ineffective.
Demonstrated robust operating characteristics when trial conduct deviated from original design specifications.

Cite This Study

Diane D. Liu (2008) studied this question.

synapsesocial.com/papers/6a8ba80298aeeda2ff892cd0 https://doi.org/10.1177/1740774508089279

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March 28, 2008Clinical Trials

A predictive probability design for phase II cancer clinical trials

View Full Paper

Ask AI

Bookmark

Authors

DLDiane D. LiuThe University of Texas MD Anderson Cancer Center

Discussion

Member takes

Implication

Methodology study demonstrates earlier trial termination for inefficacious cancer therapies, highlighting enhanced flexibility and reduced patient burden.

Key Points

Develop an adaptable phase II cancer trial design using Bayesian predictive probability that preserves rigorous statistical properties while allowing continuous patient monitoring.
Constructed a Bayesian predictive probability design incorporating minimax criteria to guide early trial termination.
Implemented a three-dimensional search algorithm to determine optimal design parameters for type I and type II error control.
Evaluated operating characteristics and performance under design deviations using exact computation and simulation studies.
Strictly controlled type I and type II error rates while enabling continuous outcome evaluation instead of rigid multi-stage checkpoints.
Yielded earlier termination and lower expected sample sizes under the null hypothesis when an experimental regimen proved ineffective.
Demonstrated robust operating characteristics when trial conduct deviated from original design specifications.

Cite This Study

Diane D. Liu (2008) studied this question.

synapsesocial.com/papers/6a8ba80298aeeda2ff892cd0 https://doi.org/10.1177/1740774508089279

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