The Sentinel Initiative mandated by the Food and Drug Administration will lead to an enormous number of studies being planned post-market that will require analyzing and combining data from several different studies. The proposed project will address this challenge through developing new and flexible methods for meta-analysis using a variety of models, including models for binary and discrete data, models for longitudinal data, and models for time-to-event data. A related issue that will also be addressed is design, sample size, and power considerations using these types of meta-analytic models. Such models and data collected post-market can be quite useful in designing future clinical studies such as non-inferiority, equivalence, and superiority cancer clinical trials. The proposed project will also develop methods for metaanalytic studies of diagnostic tests to facilitate evidence-based medicine. We will also create flexible and robust methodology for accurately comparing rare adverse event rates in cancer for different drugs and for determining how those rates are affected by important prognostic factors. The proposed project will also explore statistical methods for the analysis of large cancer data sets for calibrating treatment dose in the presence of potentially conflicting factors, such as length and quality of life and economic costs. We will explore these tradeoffs rigorously, using a utility based approach traditionally employed in the analysis of health policy at the population level. The proposed statistical methodology will be broadly applicable to complex, large scale, data sets arising in phase III clinical trials and post-marketing studies.

Public Health Relevance

The proposed statistical methodology will be broadly applicable to the statistical analysis and interpretation of complex, large scale, data sets arising in phase III clinical trials and post-marketing studies. The research will improve public health be facilitating discovery of important benefits and risks of cancer treatment.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
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Special Emphasis Panel (ZCA1)
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University of North Carolina Chapel Hill
Chapel Hill
United States
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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
Minsker, Stanislav; Zhao, Ying-Qi; Cheng, Guang (2016) Active Clinical Trials for Personalized Medicine. J Am Stat Assoc 111:875-887
Lizotte, Daniel J; Laber, Eric B (2016) Multi-Objective Markov Decision Processes for Data-Driven Decision Support. J Mach Learn Res 17:

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