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Sun, Yifei; He, Xuming; Hu, Jianhua

An omnibus test for detection of subgroup treatment effects via data partitioning. (English) Zbl 1498.62257

Ann. Appl. Stat. 16, No. 4, 2266-2278 (2022).
Summary: 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 [M. Peeters et al., “Randomized phase III study of panitumumab with fluorouracil, leucovorin, and irinotecan (FOLFIRI) compared with FOLFIRI alone as second-line treatment in patients with metastatic colorectal cancer”, Am. J. Clinic. Oncol. 28, No. 31, 4706–4713 (2010; doi:10.1200/JCO.2009.27.6055)]. 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.

Cited in 2 Documents

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis

Keywords:

bootstrap; clinical trials; data partitioning; high-dimensional covariates; subgroup treatment effect
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