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Testing interaction between treatment and high-dimensional covariates in randomized clinical trials
Andrea Callegaro1, Bart Spiessens1, Benjamin Dizier1
1GSK Vaccines, Rue de l'Institut 89, 1330, Rixensart, Belgium.
Biometrical Journal. Biometrische Zeitschrift
|October 21, 2016
Summary
This study compares methods for testing treatment interactions with many covariates in clinical trials. The Goeman global test on the interaction matrix showed promise for personalized medicine and identifying patient subgroups responsive to treatment.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Personalized Medicine
Background:
- Testing treatment interactions with numerous covariates is crucial in clinical trials.
- Identifying patient subgroups with differential treatment response is key for personalized medicine.
Purpose of the Study:
- To evaluate different statistical methods for testing treatment-covariate interactions in high-dimensional settings.
- To assess the utility of these methods for personalized medicine applications in oncology.
Main Methods:
- Univariate (marginal) model-based approaches with p-value combination.
- Dimensionality reduction using principal components (PCs) for interaction testing.
- Application of the Goeman global test to the high-dimensional interaction matrix.
Main Results:
- The Goeman global test, adjusted for main effects, was investigated for its performance.
- Methods were evaluated using simulated data to compare their effectiveness.
- The approaches were applied to real-world data from early-phase oncology clinical trials.
Conclusions:
- The Goeman global test offers a viable approach for high-dimensional interaction testing.
- These methods can aid in identifying biomarkers for personalized treatment strategies.
- The study provides insights into selecting appropriate methods for complex clinical trial analyses.
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