Evaluation of treatment-effect heterogeneity using biomarkers measured on a continuous scale: subpopulation treatment effect pattern plot

Abstract

The discovery of biomarkers that predict treatment effectiveness has great potential for improving medical care, particularly in oncology. These biomarkers are increasingly reported on a continuous scale, allowing investigators to explore how treatment efficacy varies as the biomarker values continuously increase, as opposed to using arbitrary categories of expression levels resulting in a loss of information. In the age of biomarkers as continuous predictors (eg, expression level percentage rather than positive v negative), alternatives to such dichotomized analyses are needed. 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 heterogeneity 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.

Fig 1.

Subpopulation treatment effect pattern plot analysis of the treatment effect of letrozole v tamoxifen as measured by (A) 4-year disease-free survival (DFS), (B) difference in 4-year DFS (letrozole minus tamoxifen; > zero suggested letrozole better; otherwise, tamoxifen better), and (C) hazard ratio (letrozole v tamoxifen; < one suggested letrozole better; otherwise, tamoxifen better) with corresponding 95% point-wise CIs (dashed blue lines). The x-axes indicate median percentage of Ki-67 labeling index (LI) for patients in each of the overlapping subpopulations. Each subpopulation contains approximately 150 (r₂) patients and approximately 50 (r₁) overlapping patients. Solid black lines indicate overall treatment effect, and dotted black lines indicate no effect. P values are from interaction test.

Fig 2.

Subpopulation treatment effect pattern plot analysis of the treatment effect of tamoxifen v letrozole as measured by (A) 4-year cumulative incidence of breast cancer recurrence (BCR), (B) difference in 4-year cumulative incidence of BCR (letrozole minus tamoxifen; < zero suggested letrozole better; otherwise, tamoxifen better), and (C) hazard ratio (letrozole v tamoxifen; < one suggested letrozole better; otherwise, tamoxifen better) with corresponding 95% point-wise CIs (dashed blue lines). The x-axes indicate median percentage of Ki-67 labeling index (LI) for patients in each of the overlapping subpopulations. Each subpopulation contains approximately 150 (r₂) patients and approximately 50 (r₁) overlapping patients. Solid black lines indicate overall treatment effect, and dotted black lines indicate no effect. P values are from interaction test.