Subgroup Identification Based on Quantitative Objectives
- PMID: 39551623
- DOI: 10.1002/pst.2455
Subgroup Identification Based on Quantitative Objectives
Abstract
Precision medicine is the future of drug development, and subgroup identification plays a critical role in achieving the goal. In this paper, we propose a powerful end-to-end solution squant (available on CRAN) that explores a sequence of quantitative objectives. The method converts the original study to an artificial 1:1 randomized trial, and features a flexible objective function, a stable signature with good interpretability, and an embedded false discovery rate (FDR) control. We demonstrate its performance through simulation and provide a real data example.
Keywords: biomarker; precision medicine; subgroup identification.
© 2024 John Wiley & Sons Ltd.
References
-
- M. Reck , D. Rodríguez‐Abreu , A. G. Robinson , et al., “Pembrolizumab Versus Chemotherapy for PD‐L1‐Positive Non‐Small‐Cell Lung Cancer,” New England Journal of Medicine 375, no. 19 (2016): 1823–1833, https://doi.org/10.1056/NEJMoa1606774.
-
- M. Bonetti and R. D. Gelber , “Patterns of Treatment Effects in Subsets of Patients in Clinical Trials,” Biostatistics 5, no. 3 (2004): 465–481, https://doi.org/10.1093/biostatistics/5.3.465.
-
- W. Sauerbrei , P. Royston , and K. Zapien , “Detecting an Interaction Between Treatment and a Continuous Covariate: A Comparison of Two Approaches,” Computational Statistics & Data Analysis 51, no. 8 (2007): 4054–4063.
-
- E. O. Bayman , K. Chaloner , and M. K. Cowles , “Detecting Qualitative Interaction: A Bayesian Approach,” Statistics in Medicine 29, no. 4 (2010): 455–463, https://doi.org/10.1002/sim.3787.
-
- S. Sivaganesan , P. W. Laud , and P. Müller , “A Bayesian Subgroup Analysis With a Zero‐Enriched Polya Urn Scheme,” Statistics in Medicine 30, no. 4 (2011): 312–323, https://doi.org/10.1002/sim.4108.
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