Evaluating Markers for Guiding Treatment

Abstract

Background: The subpopulation treatment effect pattern plot (STEPP) is an appealing method for assessing the clinical impact of a predictive marker on patient outcomes and identifying a promising subgroup for further study. However, its original formulation lacked a decision analytic justification and applied only to a single marker.

Methods: We derive a decision-analytic result that motivates STEPP. We discuss the incorporation of multiple predictive markers into STEPP using risk difference, cadit, and responders-only benefit functions.

Results: Applying STEPP to data from a breast cancer treatment trial with multiple markers, we found that none of the three benefit functions identified a promising subgroup for further study. Applying STEPP to hypothetical data from a trial with 100 markers, we found that all three benefit functions identified promising subgroups as evidenced by the large statistically significant treatment effect in these subgroups.

Conclusions: Because the method has desirable decision-analytic properties and yields an informative plot, it is worth applying to randomized trials on the chance there is a large treatment effect in a subgroup determined by the predictive markers.

Figure 1.

The subpopulation treatment effect pattern plot (STEPP) applied to hypothetical data for three benefit functions with one, two, or five markers. Dashed lines denote 95% confidence intervals. The horizontal axis is the quantile of the benefit score (with the benefit score in parentheses ). The vertical axis is the treatment effect measured as a difference in the probability of a binary outcome. The optimal cutpoint occurs where the lower bound of the confidence interval crosses the horizontal axis.

Figure 2.

The subpopulation treatment effect pattern plot (STEPP) applied to BIG 1-98 breast cancer data for three benefit functions with one, two, or five markers. Dashed lines denote 95% confidence intervals. The horizontal axis is the quantile of the benefit score (with the benefit score in parentheses ). The vertical axis is the treatment effect measured as a difference in the probability of disease-free survival at three years. The wide confidence intervals preclude a recommendation.