Study design is a crucial first step in clinical trials. Well-designed studies are essential for successful cancer research and cancer drug development. Innovative clinical trial designs can potentially require fewer patients, save resources, and accelerate cancer drug development. The broad, long-term objective of this research project is to develop new statistical methodology to address new and challenging issues in the design and analysis of cancer clinical trials. There are 3 specific aims in this praject.
The first aim addresses statistical methods for the design and sample size calculation for longitudinal data and joint models for longitudinal and survival data. Statistical methods will be developed for sample size and power estimation for the overall and direct treatment effect on survival, for the effect of the longitudinal process on survival, and for settings involving multivariate longitudinal and multivariate survival processes.
The second aim studies statistical methodology for the design and analysis of group randomized cancer prevention trials with survival and recurrent event outcomes. Empirical process theory will be used to study the asymptotic behavior of the test statistics and both asymptotic approximation as well as permutation test will be used to develop sample size formulas and power estimation.
The third aim addresses important statistical issues in the oncology drug development pathway. There are three sub-aims. The first sub-aim is in the area of targeted designs. Methods for alternative designs, including "enrichment" designs, will be developed, and the operating characteristics and costs of these designs to fully targeted designs will be compared. Valid and efficient statistical methods for these trials will be developed by applying a semiparametric empirical likelihood approach. The second sub-aim is in the area of phase 11 designs. New methods for phase II and phase 11/111 clinical trials will be developed and their operating characteristics, costs, and predictive ability for subsequent phase HI trials will be assessed. Information on both combination and non-combination therapies in phase 11 studies and subsequent phase III studies will be gathered to build prediction models using machine learning and other nonparametric classification methods. The third sub-aim is in the area of partially randomized designs. New semiparametric empirical likelihood methods will be developed for the design and analysis of such trials to adjust for selection bias and to improve efficiency. Our research will produce important new and efficient design and analysis tools for cancer research.

Public Health Relevance

This research will provide valuable new design and analysis tools to cancer researchers and other biomedical researchers. These new and improved design and analysis tools will help to improve the quality and efficiency of cancer clinical trials. They will help to improve public health by enabling accurate and efficient estimation of sample size and power calculation for cancer clinical trials and by accelerating cancer drug development.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1-RPRB-7)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of North Carolina Chapel Hill
Chapel Hill
United States
Zip Code
Acharya, Chaitanya R; McCarthy, Janice M; Owzar, Kouros et al. (2016) Exploiting expression patterns across multiple tissues to map expression quantitative trait loci. BMC Bioinformatics 17:257
Laber, Eric B; Zhao, Ying-Qi; Regh, Todd et al. (2016) Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy. Stat Med 35:1245-56
Li, Zhiguo; Owzar, Kouros (2016) Fitting Cox Models with Doubly Censored Data Using Spline-Based Sieve Marginal Likelihood. Scand Stat Theory Appl 43:476-486
Wang, Xiaofei; Berry, Mark F (2016) Risk calculators are useful but.... J Thorac Cardiovasc Surg 151:706-7
Wang, Xuefeng; Chen, Mengjie; Yu, Xiaoqing et al. (2016) Global copy number profiling of cancer genomes. Bioinformatics 32:926-8
Ivanova, Anastasia; Wang, Yunfei; Foster, Matthew C (2016) The rapid enrollment design for Phase I clinical trials. Stat Med 35:2516-24
Zhang, Daowen; Sun, Jie Lena; Pieper, Karen (2016) Bivariate Mixed Effects Analysis of Clustered Data with Large Cluster Sizes. Stat Biosci 8:220-233
Schifano, Elizabeth D; Wu, Jing; Wang, Chun et al. (2016) Online Updating of Statistical Inference in the Big Data Setting. Technometrics 58:393-403
Lizotte, Daniel J; Laber, Eric B (2016) Multi-Objective Markov Decision Processes for Data-Driven Decision Support. J Mach Learn Res 17:
Minsker, Stanislav; Zhao, Ying-Qi; Cheng, Guang (2016) Active Clinical Trials for Personalized Medicine. J Am Stat Assoc 111:875-887

Showing the most recent 10 out of 378 publications