December 2022 An omnibus test for detection of subgroup treatment effects via data partitioning
Yifei Sun, Xuming He, Jianhua Hu
Author Affiliations +
Ann. Appl. Stat. 16(4): 2266-2278 (December 2022). DOI: 10.1214/21-AOAS1589


Late-stage clinical trials have been conducted primarily to establish the efficacy of a new treatment in an intended population. A corollary of population heterogeneity in clinical trials is that a treatment might be effective for one or more subgroups, rather than for the whole population of interest. As an example, the phase III clinical trial of panitumumab in metastatic colorectal cancer patients failed to demonstrate its efficacy in the overall population, but a subgroup associated with tumor KRAS status was found to be promising (Peeters et al. (Am. J. Clin. Oncol. 28 (2010) 4706–4713)). As we search for such subgroups via data partitioning based on a large number of biomarkers, we need to guard against inflated type I error rates due to multiple testing. Commonly-used multiplicity adjustments tend to lose power for the detection of subgroup treatment effects. We develop an effective omnibus test to detect the existence of, at least, one subgroup treatment effect, allowing a large number of possible subgroups to be considered and possibly censored outcomes. Applied to the panitumumab trial data, the proposed test would confirm a significant subgroup treatment effect. Empirical studies also show that the proposed test is applicable to a variety of outcome variables and maintains robust statistical power.

Funding Statement

Sun’s research is partially supported by the National Institute of Health (NCI 5P30 CA013696 and NIA 2U19AG033655).
He’s research is partially supported by NSF Award DMS-1914496.
Hu’s research is partially supported by the National Institute of Health (NCI 5P30 CA013696, NIAID 1R01 AI143886, NIH/NCI 1R01 CA219896, NCI P01 CA098101).


The authors would like to thank the referees, an Associate Editor, and the Editor for their constructive comments that improved the quality of this paper.


Download Citation

Yifei Sun. Xuming He. Jianhua Hu. "An omnibus test for detection of subgroup treatment effects via data partitioning." Ann. Appl. Stat. 16 (4) 2266 - 2278, December 2022.


Received: 1 May 2021; Revised: 1 October 2021; Published: December 2022
First available in Project Euclid: 26 September 2022

MathSciNet: MR4489209
zbMATH: 1498.62257
Digital Object Identifier: 10.1214/21-AOAS1589

Keywords: bootstrap , Clinical trials , data partitioning , High-dimensional covariates , subgroup treatment effect

Rights: Copyright © 2022 Institute of Mathematical Statistics


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Vol.16 • No. 4 • December 2022
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