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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA142538-05
Application #
8643482
Study Section
Special Emphasis Panel (ZCA1-RPRB-7)
Project Start
Project End
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
5
Fiscal Year
2014
Total Cost
$294,494
Indirect Cost
$42,035
Name
University of North Carolina Chapel Hill
Department
Type
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Wang, Xiaofei; Wang, Xiaoyi; Hodgson, Lydia et al. (2017) Validation of Progression-Free Survival as a Surrogate Endpoint for Overall Survival in Malignant Mesothelioma: Analysis of Cancer and Leukemia Group B and North Central Cancer Treatment Group (Alliance) Trials. Oncologist 22:189-198
Zhang, Danjie; Chen, Ming-Hui; Ibrahim, Joseph G et al. (2017) Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical Trials. J Comput Graph Stat 26:121-133
Kang, Suhyun; Lu, Wenbin; Song, Rui (2017) Subgroup detection and sample size calculation with proportional hazards regression for survival data. Stat Med 36:4646-4659
Zhao, Jingkang; Li, Dongshunyi; Seo, Jungkyun et al. (2017) Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding. Res Comput Mol Biol 10229:336-352
Silva, Grace O; Siegel, Marni B; Mose, Lisle E et al. (2017) SynthEx: a synthetic-normal-based DNA sequencing tool for copy number alteration detection and tumor heterogeneity profiling. Genome Biol 18:66
Stinchcombe, Thomas E; Zhang, Ying; Vokes, Everett E et al. (2017) Pooled Analysis of Individual Patient Data on Concurrent Chemoradiotherapy for Stage III Non-Small-Cell Lung Cancer in Elderly Patients Compared With Younger Patients Who Participated in US National Cancer Institute Cooperative Group Studies. J Clin Oncol 35:2885-2892
Li, Zhiguo (2017) Comparison of adaptive treatment strategies based on longitudinal outcomes in sequential multiple assignment randomized trials. Stat Med 36:403-415
Ding, Jieli; Lu, Tsui-Shan; Cai, Jianwen et al. (2017) Recent progresses in outcome-dependent sampling with failure time data. Lifetime Data Anal 23:57-82
Liang, Baosheng; Tong, Xingwei; Zeng, Donglin et al. (2017) SEMIPARAMETRIC REGRESSION ANALYSIS OF REPEATED CURRENT STATUS DATA. Stat Sin 27:1079-1100
Kong, Dehan; Maity, Arnab; Hsu, Fang-Chi et al. (2017) Rejoinder to ""A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine"". Biometrics :

Showing the most recent 10 out of 462 publications