Biomarker threshold adaptive designs for survival endpoints.

TitleBiomarker threshold adaptive designs for survival endpoints.
Publication TypeJournal Article
Year of Publication2018
AuthorsDiao, Guoqing, Jun Dong, Donglin Zeng, Chunlei Ke, Alan Rong, and Joseph G. Ibrahim
JournalJ Biopharm Stat
Date Published2018
KeywordsAlgorithms, Antineoplastic Agents, Antineoplastic Agents, Immunological, Biomarkers, Tumor, Biostatistics, Clinical Decision-Making, Clinical Trials, Phase III as Topic, Computer Simulation, Data Interpretation, Statistical, ErbB Receptors, Head and Neck Neoplasms, Humans, Models, Statistical, Neoplasms, Panitumumab, Patient Selection, Precision Medicine, Predictive Value of Tests, PTEN Phosphohydrolase, Randomized Controlled Trials as Topic, Research Design, Squamous Cell Carcinoma of Head and Neck, Survival Analysis, Time Factors, Treatment Outcome

Due to the importance of precision medicine, it is essential to identify the right patients for the right treatment. Biomarkers, which have been commonly used in clinical research as well as in clinical practice, can facilitate selection of patients with a good response to the treatment. In this paper, we describe a biomarker threshold adaptive design with survival endpoints. In the first stage, we determine subgroups for one or more biomarkers such that patients in these subgroups benefit the most from the new treatment. The analysis in this stage can be based on historical or pilot studies. In the second stage, we sample subjects from the subgroups determined in the first stage and randomly allocate them to the treatment or control group. Extensive simulation studies are conducted to examine the performance of the proposed design. Application to a real data example is provided for implementation of the first-stage algorithms.

Alternate JournalJ Biopharm Stat
Original PublicationBiomarker threshold adaptive designs for survival endpoints.
PubMed ID29436940
PubMed Central IDPMC6342463
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States