title: Design and statistical principles of the SHIVA trial.
abstract: Most molecularly targeted agents (MTAs) are expected to work in subgroups of cancer patients characterized by the presence of molecular alterations in the tumor cells. However, clinical development is generally carried out according to tumor type. The SHIVA randomized trial on the contrary has been set up to investigate which of tumor biology or tumor location and histology is the most important to select treatment in patients with cancer refractory to standard of care. Statistical principles, specificities, strengths and limitations of this trial that evaluates an omic-based algorithm to select the targeted agent are reviewed. In particular, the need for a randomized trial where the various steps to build the algorithm are explicitly described and standardized is emphasized. The impact of an algorithm that would be partly misspecified (i.e., that would lead to correct treatment selection for some tumor molecular profile but not for all) is quantified.
authors.affiliation: Department of Biostatistics and Epidemiology, Gustave Roussy Paris Campus, 114 Rue Ed Vaillant, 94805 Villejuif Cedex, France. xavier.paoletti@gustaveroussy.fr.
authors.forename: Xavier
authors.initials: X
authors.lastname: Paoletti
authors.affiliation: Department of Biostatistics, Institut Curie, Paris, France; Unité INSERM, Institut Curie, Mines ParisTech U900, Paris, France.
authors.forename: Bernard
authors.initials: B
authors.lastname: Asselain
authors.affiliation: Department of Medical Oncology, Clinical Trial Unit, Institut Curie, Paris and Saint-Cloud, France.
authors.forename: Maud
authors.initials: M
authors.lastname: Kamal
authors.affiliation: Unité INSERM, Institut Curie, Mines ParisTech U900, Paris, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Servant
authors.affiliation: Unité INSERM, Institut Curie, Mines ParisTech U900, Paris, France.
authors.forename: Philippe
authors.initials: P
authors.lastname: Huppé
authors.affiliation: Unité de Pharmacogénomique, Service de Génétique, Institut Curie, Paris, France.
authors.forename: Ivan
authors.initials: I
authors.lastname: Bieche
authors.affiliation: Unité INSERM, Institut Curie, Mines ParisTech U900, Paris, France; Department of Medical Oncology, Clinical Trial Unit, Institut Curie, Paris and Saint-Cloud, France.
authors.forename: Christophe
authors.initials: C
authors.lastname: Le Tourneau
mesh_terms: D000465:Algorithms; D017322:Clinical Trials, Phase II as Topic / Q000379:methods / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D019008:Drug Resistance, Neoplasm; D006801:Humans; D058990:Molecular Targeted Therapy / Q000379:methods / Q000706:statistics & numerical data; D009369:Neoplasms / Q000188:drug therapy / Q000453:epidemiology; D057285:Precision Medicine / Q000379:methods / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000037:antagonists & inhibitors / Q000276:immunology; D012107:Research Design
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases
keywords: Heterogeneity; omic-based algorithm; precision medicine; randomization