The aim is the development and evaluation of improved statistical methods for a range of medical and biomedical studies. The proposal incorporates ongoing research efforts in the development of statistical methods for epidemiologic cohort and case-control studies, and in the development of statistical methods for therapeutic studies, along with new research emphasis on disease prevention/risk factor intervention trials and on certain types of animal studies, to form a program project proposal. Specific research topics in the disease prevention/risk factor intervention area include the use of a case-cohort design to avoid expensive assembly of covariate histories on the entire trial cohort, the development of data analysis procedures to accommodate correlated endpoint data as may arise, for example, if subjects are randomized in groups, and the development of designs and procedures to avoid control group contamination. Topics in the analysis of epidemiologic studies will include the further development of relative risk regression and odds ratio regression methods with emphasis on such sub-topics as linear relative risk estimation, interaction testing with sparse data, and evaluation of the impact of covariate measurement errors. Methodologic issues in genetic epidemiology and in the analysis of ordered categorical data will also be considered. The work directed toward therapeutic trials will emphasize study design issues, including randomization strategies, and sequential designs; exploratory data analysis issues, including diagnostics, graphical displays and median survival curve estimation; and data analytic issues, including study of the use of multistate and multivariate models. The statistical methods for animal carcinogenicity testing project will examine the use of parametric models to compare tumor incidence rates based on death and sacrifice data, will evaluate testing procedures currently in use and will propose alternate procedures that may, for example, incorporate historical control information.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
9P01CA053996-14
Application #
3094536
Study Section
Special Emphasis Panel (SRC (U2))
Project Start
1991-01-15
Project End
1995-12-31
Budget Start
1991-01-15
Budget End
1991-12-31
Support Year
14
Fiscal Year
1991
Total Cost
Indirect Cost
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
075524595
City
Seattle
State
WA
Country
United States
Zip Code
98109
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
Prentice, Ross L; Zhao, Shanshan (2018) Nonparametric estimation of the multivariate survivor function: the multivariate Kaplan-Meier estimator. Lifetime Data Anal 24:3-27

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