biostudies-literatureUnknown108(9)Baker SGThe 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. 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. 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 beJournal of the National Cancer Institutedjw101https://www.ebi.ac.uk/biostudies/studies/S-EPMC5939825biostudies-literatureEvaluating Markers for Guiding Treatment.PMC5939825Bonetti MBaker SGfalseEvaluating Markers for Guiding Treatment.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. 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. 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 be2016-01-01T00:00:00Z2016 Sep2025-04-22T01:39:32.867Z2019-03-26T23:47:29ZS-EPMC59398252719377210.1093/jnci/djw101