Randomized clinical trials are and will continue to be the key vehicle for evaluation of new and existing cancer therapies. This revolutionary era of advances in the biological sciences is leading to the discovery of novel biomarkers and complex genetic and genomic information that may be highly associated with various clinical outcomes, offering the tantalizing opportunity to exploit this information to both improve the precision of the analyses of trials and to develop models of longitudinal disease progression that may reveal important insights. A recurrent challenge is that missing data and subject drop-out are commonplace, presenting complications for analyses of these trials. Through a series of aims addressing these issues, this project proposes research that will have a significant impact on the quality and strength of inferences possible from current cancer clinical trials. That it is possible to improve efficiency of primary analyses of clinical trials by exploiting prognostic baseline auxiliary information is well known;however, such analyses are controversial because of the temptation to choose the analysis that leads to the most dramatic treatment effect. In the first aim, new methods for such "covariate adjustment" will be studied that circumvent this issue and can improve over existing approaches. In the second aim, these methods will be extended so that they may be used in the common case where outcomes are missing due to drop-out. Efficient methods for longitudinal analysis of measures such as quality of life and biomarkers in the presence of drop-out will also be developed. Understanding the relationship between such longitudinal measures and clinical outcomes such as time to recurrence or survival time is of key importance.
The third aim focuses on development of methods for assessing the correctness of so-called joint statistical models used for this purpose and for assessing the influence of particular observations on the fit ofthe model, where the data used to develop the model may be missing. Finally, taking appropriate account of missing data sometimes requires unverifiable assumptions about why the data are missing, which are incorporated in models that thus cannot be checked based on the data.
The fourth aim i s devoted to development of a new statistical framework for assessing how sensitive conclusions are to the modeling assumptions made.
Randomized clinical trials in cancer research are the most important mechanism for the evaluation of new and existing therapies. Statistical methods will be developed that will improve the precision of the analyses of these trials and provide tools for drawing valid conclusions when some of the data intended to be collected are missing, e.g., if some subjects drop out ofthe trial, offering cancer researchers an expanded set of tools that will greatly improve the quality and strength of analyses of current cancer clinical trials.
|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