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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Simulation-based adjustment after exploratory biomarker subgroup selection in phase II.

Heiko Götte¹, Marietta Kirchner², Martin Oliver Sailer³

¹Merck KGaA, Darmstadt, Germany.

Statistics in Medicine

|April 25, 2017

Summary

This summary is machine-generated.

This study introduces a bias adjustment method using Approximate Bayesian Computation for phase III trial selection. The simulation-based approach reduces over-optimistic expectations by correcting for subgroup analysis bias in targeted therapy trials.

Keywords:

ABC bias adjustment posterior approximation probability of success subgroup selection

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Area of Science:

Biostatistics
Clinical Trial Design
Pharmacogenomics

Background:

Phase II trials commonly use exploratory subgroup analyses to identify patient populations benefiting from targeted therapies.
Biomarker-defined subgroups are frequently selected for subsequent phase III trials based on promising phase II results.
Selecting subgroups based on large observed effects can lead to upward bias and over-optimistic phase III success probability estimates.

Purpose of the Study:

To develop and evaluate a simulation-based bias adjustment method for subgroup selection in clinical trials.
To mitigate the upward bias introduced by selecting patient subgroups based on observed treatment effects in phase II trials.
To provide recommendations for implementing this bias adjustment technique in oncology trials.

Main Methods:

Utilized Approximate Bayesian Computation (ABC) techniques to create a simulation-based bias adjustment framework.
Conducted simulation studies to compare the proposed method against the maximum likelihood estimator.
Applied the bias adjustment procedure to real-world data from an oncology clinical trial.

Main Results:

The proposed Approximate Bayesian Computation-based method significantly reduces bias compared to the maximum likelihood estimator.
Simulation studies demonstrated the effectiveness of the bias adjustment in correcting for selection-induced overestimation.
The method provides a practical approach for refining phase III trial success probability estimates.

Conclusions:

The developed simulation-based bias adjustment method using ABC is effective in addressing the upward bias from subgroup selection.
This approach offers a more realistic estimation of phase III trial success probabilities for targeted therapies.
Implementation of this method can improve decision-making regarding patient subgroup selection for late-stage clinical trials.