title: Biomarker-adaptive threshold design: a procedure for evaluating treatment with possible biomarker-defined subset effect.
abstract: BACKGROUND
  Many molecularly targeted anticancer agents entering the definitive stage of clinical development benefit only a subset of treated patients. This may lead to missing effective agents by the traditional broad-eligibility randomized trials due to the dilution of the overall treatment effect. We propose a statistically rigorous biomarker-adaptive threshold phase III design for settings in which a putative biomarker to identify patients who are sensitive to the new agent is measured on a continuous or graded scale.
  
  
  METHODS
  The design combines a test for overall treatment effect in all randomly assigned patients with the establishment and validation of a cut point for a prespecified biomarker of the sensitive subpopulation. The performance of the biomarker-adaptive design, relative to a traditional design that ignores the biomarker, was evaluated in a simulation study. The biomarker-adaptive design was also used to analyze data from a prostate cancer trial.
  
  
  RESULTS
  In the simulation study, the biomarker-adaptive design preserved the power to detect the overall effect when the new treatment is broadly effective. When the proportion of sensitive patients as identified by the biomarker is low, the proposed design provided a substantial improvement in efficiency compared with the traditional trial design. Recommendations for sample size planning and implementation of the biomarker-adaptive design are provided.
  
  
  CONCLUSIONS
  A statistically valid test for a biomarker-defined subset effect can be prospectively incorporated into a randomized phase III design without compromising the ability to detect an overall effect if the intervention is beneficial in a broad population.
authors.affiliation: Biometric Research Branch, Division of Cancer Treatment and Diagnosis, EPN-8122, National Cancer Institute, Bethesda, MD 20892, USA.
authors.forename: Wenyu
authors.initials: W
authors.lastname: Jiang
authors.forename: Boris
authors.initials: B
authors.lastname: Freidlin
authors.forename: Richard
authors.initials: R
authors.lastname: Simon
mesh_terms: D000465:Algorithms; D016588:Anticarcinogenic Agents / Q000494:pharmacology; D014408:Biomarkers, Tumor / Q000378:metabolism*; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004341:Drug Evaluation; D004353:Drug Evaluation, Preclinical; D023381:Endpoint Determination; D006801:Humans; D008297:Male; D009369:Neoplasms / Q000097:blood* / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000188:drug therapy; D012107:Research Design; D018401:Sample Size
chemical_list: D016588:Anticarcinogenic Agents; D014408:Biomarkers, Tumor