title: Evaluating Markers for Guiding Treatment.
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 benefit functions identified promising subgroups as evidenced by the large statistically significant treatment effect in these subgroups.
  
  
  
  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.
authors.affiliation: Division of Cancer Prevention, National Cancer Institute, Bethesda, MD (SGB); Carlo F. Dondena Centre for Research on Social Dynamics and Public Policies and Bocconi University, Milan, Italy (MB) sb16i@nih.gov.
authors.forename: Stuart G
authors.initials: SG
authors.lastname: Baker
authors.affiliation: Division of Cancer Prevention, National Cancer Institute, Bethesda, MD (SGB); Carlo F. Dondena Centre for Research on Social Dynamics and Public Policies and Bocconi University, Milan, Italy (MB).
authors.forename: Marco
authors.initials: M
authors.lastname: Bonetti
mesh_terms: D000367:Age Factors; D018931:Antineoplastic Agents, Hormonal / Q000627:therapeutic use; D014408:Biomarkers, Tumor; D015992:Body Mass Index; D001943:Breast Neoplasms / Q000737:chemistry / Q000188:drug therapy* / Q000473:pathology*; D000066491:Clinical Decision-Making / Q000379:methods; D003661:Decision Support Techniques; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D019394:Ki-67 Antigen / Q000032:analysis; D000077289:Letrozole; D016015:Logistic Models; D008207:Lymphatic Metastasis; D009570:Nitriles / Q000627:therapeutic use; D011237:Predictive Value of Tests; D016032:Randomized Controlled Trials as Topic; D011960:Receptors, Estrogen / Q000032:analysis; D011980:Receptors, Progesterone / Q000032:analysis; D013629:Tamoxifen / Q000627:therapeutic use; D014230:Triazoles / Q000627:therapeutic use; D047368:Tumor Burden
chemical_list: D018931:Antineoplastic Agents, Hormonal; D014408:Biomarkers, Tumor; D019394:Ki-67 Antigen; D009570:Nitriles; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; D014230:Triazoles; D013629:Tamoxifen; D000077289:Letrozole