Efficient oncology clinical trial design requires optimal endpoint selection. Previous standard oncologic endpoints of binary dose-limiting toxicity in phase I, dichotomous tumor response in phase II, and overall survival in phase III are all being appropriately challenged as inefficient, imprecise, or impractical. These endpoints require reconsideration in the current era, where therapies are increasingly targeted, are sub- population specific, have non-cytotoxic mechanisms of action, and where, in many diseases, multiple endpoints or effective lines of treatment exist. In this project we propose 3 highly interactive yet distinct specific aims related to the development, validation, and implementation of novel endpoints in oncology clinical trials: 1) novel use of continuous metrics of adverse events (including multiple event types and cycles of treatment) alone and in concert with preliminary efficacy measures as phase I trial endpoints; 2) new and refined methods to evaluate potential surrogate endpoints at both the individual patient and trial levels; and 3) implementation of novel phase II and III trial designs where multiple endpoints and/or predictive biomarkers exist.
Specific Aim 1 will expand existing expertise in adaptive dose-finding designs by incorporating multivariate toxicity endpoints and using bivariate endpoints incorporating both toxicity and preliminary efficacy measures.
In Specific Aim 2, novel methods for surrogate endpoint evaluation will be developed and rigorously tested using an already developed robust simulation engine. A novel design proposed by our group, the first to address the critical absence of implementation strategies for surrogate endpoints through a trial design making explicit and monitored use of a newly validated surrogate as its primary endpoint, will be enhanced in Specific Aim 3 to account for multiple putative surrogate endpoints or multiple biomarker-based subgroups with heterogeneous multi-endpoint performance within a new phase II or III trial.
Each aim builds on existing expertise and strong preliminary data to advance the field in a novel yet practical manner that can be readily translated into clinical trial practice, taking advantage of the unique data and trial resources available at the Mayo Clinic. In particular, the research team at the Mayo Clinic under Dr. Sargent's leadership has the un-paralleled opportunity to directly translate these methodological advances in novel designs and endpoints into practice through implementation in upcoming oncology clinical trials.

Public Health Relevance

This project will develop methods to assess and implement new endpoints for Phase I, II, and III clinical trials in oncology, to hasten the progress of cance therapy research. The research will draw upon unparalleled data resources available at the Mayo Clinic, and take advantage of the direct ability to translate the findings into clinical trial due to the applicant's leadership role within multiple long-standing oncology research collaborations.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Witherspoon, Kim
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Mayo Clinic, Rochester
United States
Zip Code
Yin, Jun; Paoletti, Xavier; Sargent, Daniel J et al. (2017) Repeated measures dose-finding design with time-trend detection in the presence of correlated toxicity data. Clin Trials 14:611-620
Yin, Jun; Qin, Rui; Ezzalfani, Monia et al. (2017) A Bayesian dose-finding design incorporating toxicity data from multiple treatment cycles. Stat Med 36:67-80
Renfro, Lindsay A; An, Ming-Wen; Mandrekar, Sumithra J (2017) Precision oncology: A new era of cancer clinical trials. Cancer Lett 387:121-126
Renfro, L A; Sargent, D J (2016) Statistical controversies in clinical research: basket trials, umbrella trials, and other master protocols: a review and examples. Ann Oncol :
Renfro, Lindsay A; Mallick, Himel; An, Ming-Wen et al. (2016) Clinical trial designs incorporating predictive biomarkers. Cancer Treat Rev 43:74-82
Renfro, Lindsay A; Shang, Hongwei; Sargent, Daniel J (2015) Impact of Copula Directional Specification on Multi-Trial Evaluation of Surrogate End Points. J Biopharm Stat 25:857-77
Janes, Holly; Pepe, Margaret S; McShane, Lisa M et al. (2015) The Fundamental Difficulty With Evaluating the Accuracy of Biomarkers for Guiding Treatment. J Natl Cancer Inst 107:
Renfro, Lindsay A; Shi, Qian; Xue, Yuan et al. (2014) Center-Within-Trial Versus Trial-Level Evaluation of Surrogate Endpoints. Comput Stat Data Anal 78:1-20