Background: Primary results from randomized clinical trials (RCT) only inform on the average treatment effect in the studied population, and it is critical to understand how treatment effect varies across subpopulations. In this paper we describe a clustering-based approach for the assessment of Heterogeneity of Treatment Effect (HTE) over patient phenotypes, which maintains the unsupervised nature of classical subgroup analysis while jointly accounting for relevant patient characteristics.
Methods: We applied phenotype-based stratification in the ENGAGE AF-TIMI 48 trial, a non-inferiority trial comparing the effects of higher-dose edoxaban regimen (direct anticoagulant) versus warfarin (vitamin K antagonist) on a composite endpoint of stroke and systemic embolism in 14,062 patients with atrial fibrillation.
Results: We identified three distinct phenotypes: non-white participants, mostly from Asia (A); white participants without previous use of vitamin-K antagonists (B); and white participants with previous use of vitamin-K antagonist (C). The effect of the higher-dose edoxaban regimen vs warfarin significantly varied over phenotypes (p for interaction = 0.03) with the strongest benefit in cluster A (HR = 0.72, 95 % CI: 0.52-1.00), moderate effect in cluster B (HR = 0.80, 95 % CI: 0.61, 1.06) and no observed effect in cluster C (HR = 1.01, 95 % CI: 0.80, 1.27).
Conclusions: Assessing HTE over patients' phenotypes might represent a relevant complement to other stratification approaches to elucidate results from subgroups analyses, especially in those settings where an overwhelming superiority overall effect was not observed. Cluster analysis allows a clear discrimination of patients with direct interpretability of who are the patients that would most benefit from the investigated strategy or treatment.
Keywords: Clinical trials; Cluster analysis; Machine learning; Precision medicine.
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