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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Group sequential enrichment design incorporating subgroup selection.

Baldur P Magnusson¹, Bruce W Turnbull

¹Novartis Pharma AG, Basel, Switzerland. baldur.magnusson@novartis.com

Statistics in Medicine

|January 15, 2013

Summary

This study introduces an adaptive group sequential design for clinical trials, focusing resources on patient subgroups likely to benefit from treatment. This method enhances statistical power and efficiency in drug development while controlling errors.

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Area of Science:

Clinical Trial Design
Biostatistics
Pharmacometrics

Background:

Analyzing treatment efficacy in patient subgroups is crucial but statistically challenging in clinical trials.
Multiplicity concerns and small subgroup sample sizes can lead to misleading efficacy conclusions.
Existing methods may not adequately address subgroup heterogeneity or resource allocation.

Purpose of the Study:

To propose a novel adaptive enrichment group sequential procedure for confirmatory phase II/III clinical trials.
To concentrate resources on patient subgroups with the highest likelihood of treatment response.
To enhance statistical power and efficiency in subgroup analyses while maintaining error rate control.

Main Methods:

An adaptive enrichment group sequential design utilizing upper and lower spending functions for stopping boundaries.
The procedure is based on the efficient score, applicable to normal, binary, and time-to-event data.
Employs a bootstrap algorithm for bias-adjusted point and interval estimates, addressing selection bias.

Main Results:

The proposed design effectively identifies and eliminates non-beneficial subgroups early, mitigating the dilution effect.
Demonstrates high power for detecting subgroup-specific treatment effects.
Significant sample size savings are achievable through multiple interim analyses and adaptive design features.

Conclusions:

The adaptive enrichment group sequential design offers a robust and efficient approach for subgroup analyses in clinical trials.
Provides strong protection of the familywise type I error rate.
Outperforms traditional group sequential tests and adaptive two-stage procedures in detecting subgroup effects and saving resources.