This renewal application proposes to carry out a Program of statistical methods research to address gaps and barriers arising in three biomedical research sectors: Project 1, 'Chronic Disease Population Science Research Issues and Strategies', aims to develop methods that are mostly pertinent to the prevention of cancer and other chronic diseases. These include methods for the analysis of multivariate failure time and longitudinal data, and for disease risks attribution; methods for correcting dietary and physical activity assessment data using biomarkers, and for new biomarker development; methods for using high dimensional genotype data to identify the preferred treatment or intervention for individuals; and methods for biological network development and for preventive intervention development. Project 2, 'Genetic Epidemiology Methods', focuses primarily on methods needed to more fully understand the genetic contribution to disease risk in the post-genome wide association study era. These include methods for identifying combinations of environmental factors that modify genetic effects; methods for rare variant association studies; methods for penetrance function estimation; and methods for using genotype data to facilitate environmental factor association studies. Project 3, 'Use of Biomarkers in Diagnosis, Prognosis, Risk Prediction and Early Detection of Disease', proposes to develop novel study designs for prognostic biomarker evaluation to improve inference on ROC curves through the use of standardized biomarker values; and to develop group sequential design procedures for biomarker evaluation. Collectively, these projects will apply the talents of 15 active biostatistical methodologists, in an interactive and coordinated manner, to address statistical issues that are among the most important for progress in chronic disease population research.

Public Health Relevance

This Program proposes to develop statistical techniques and research strategies to address gaps and barriers in biomedical research on cancer and other chronic diseases. Specific projects propose to develop needed methodology for chronic disease prevention research; for genetic epidemiology research; and for disease prognosis research, through applying the collective talents of 15 committed biostatistical investigators.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA053996-38
Application #
8915061
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Mariotto, Angela B
Project Start
1997-01-01
Project End
2017-06-30
Budget Start
2015-07-01
Budget End
2017-06-30
Support Year
38
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
Prentice, Ross L; Zhao, Shanshan (2018) Nonparametric estimation of the multivariate survivor function: the multivariate Kaplan-Meier estimator. Lifetime Data Anal 24:3-27
Howard, Barbara V; Aragaki, Aaron K; Tinker, Lesley F et al. (2018) A Low-Fat Dietary Pattern and Diabetes: A Secondary Analysis From the Women's Health Initiative Dietary Modification Trial. Diabetes Care 41:680-687
Huang, Yijian; Wang, Ching-Yun (2018) Cox regression with dependent error in covariates. Biometrics 74:118-126
Su, Yu-Ru; Di, Chongzhi; Bien, Stephanie et al. (2018) A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics. Am J Hum Genet 102:904-919
Liu, Dandan; Cai, Tianxi; Lok, Anna et al. (2018) Nonparametric Maximum Likelihood Estimators of Time-Dependent Accuracy Measures for Survival Outcome Under Two-Stage Sampling Designs. J Am Stat Assoc 113:882-892
Yu, Hsiang; Cheng, Yu-Jen; Wang, Ching-Yun (2018) Methods for multivariate recurrent event data with measurement error and informative censoring. Biometrics 74:966-976
Monaco, John V; Gorfine, Malka; Hsu, Li (2018) General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv. J Stat Softw 86:
Dai, James Y; Wang, Xiaoyu; Buas, Matthew F et al. (2018) Whole-genome sequencing of esophageal adenocarcinoma in Chinese patients reveals distinct mutational signatures and genomic alterations. Commun Biol 1:174
Dai, James Y; Peters, Ulrike; Wang, Xiaoyu et al. (2018) Diagnostics for Pleiotropy in Mendelian Randomization Studies: Global and Individual Tests for Direct Effects. Am J Epidemiol 187:2672-2680
Dai, James Y; Liang, C Jason; LeBlanc, Michael et al. (2018) Case-only approach to identifying markers predicting treatment effects on the relative risk scale. Biometrics 74:753-763

Showing the most recent 10 out of 319 publications