The purpose of this article is to provide an overview of an intuitive statistical approach— the subpopulation treatment effect pattern plot (STEPP)—for evaluating treatment-effect hetero- geneity when a biomarker is measured on a continuous scale. STEPP graphically explores the patterns of treatment effect across overlapping intervals of the biomarker values. As an example, STEPP methodology is used to explore patterns of treatment effect for varying levels of the biomarker Ki-67 in the BIG (Breast International Group) 1-98 randomized clinical trial comparing letrozole with tamoxifen as adjuvant therapy for postmenopausal women with hormone receptor– positive breast cancer. STEPP analyses showed patients with higher Ki-67 values who were assigned to receive tamoxifen had the poorest prognosis and may benefit most from letrozole.
Evaluation of Treatment-Effect Heterogeneity Using Biomarkers Measured on a Continuous Scale: Subpopulation Treatment-Effect Pattern Plot (STEPP).
BONETTI, MARCO;
2010
Abstract
The purpose of this article is to provide an overview of an intuitive statistical approach— the subpopulation treatment effect pattern plot (STEPP)—for evaluating treatment-effect hetero- geneity when a biomarker is measured on a continuous scale. STEPP graphically explores the patterns of treatment effect across overlapping intervals of the biomarker values. As an example, STEPP methodology is used to explore patterns of treatment effect for varying levels of the biomarker Ki-67 in the BIG (Breast International Group) 1-98 randomized clinical trial comparing letrozole with tamoxifen as adjuvant therapy for postmenopausal women with hormone receptor– positive breast cancer. STEPP analyses showed patients with higher Ki-67 values who were assigned to receive tamoxifen had the poorest prognosis and may benefit most from letrozole.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.