{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["108(9)"],"submitter":["Baker SG"],"pubmed_abstract":["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 be"],"journal":["Journal of the National Cancer Institute"],"pagination":["djw101"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5939825"],"repository":["biostudies-literature"],"pubmed_title":["Evaluating Markers for Guiding Treatment."],"pmcid":["PMC5939825"],"pubmed_authors":["Bonetti M","Baker SG"],"additional_accession":[]},"is_claimable":false,"name":"Evaluating Markers for Guiding Treatment.","description":"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 be","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016 Sep","modification":"2025-04-22T01:39:32.867Z","creation":"2019-03-26T23:47:29Z"},"accession":"S-EPMC5939825","cross_references":{"pubmed":["27193772"],"doi":["10.1093/jnci/djw101"]}}