This proposal revolves around the development of new statistical methods and their application to studies involving cancer and nutrition. The following broad topics will be considered. Diet and Colon Carcinogenesis: We will develop semi-parametric statistical methods for hierarchical functional data to analyze a new series of studies, done at the cellular level, involving diet, apoptosis, cellular response and colon carcinogenesis. Our approach allows understanding of the effects of cell position in the colonic crypts, as well as incorporating crypt signaling, i.e., correlations of response among the crypts themselves. A special case of our approach includes new hierarchical functional measurement error models. Analysis of Dietary Intake Data: In conjunction with researchers at the NCI, we have developed access to a number of exciting dietary intake data sets, including a major biomarker study, a major surveillance study and a major prospective cohort study. Our research includes the use of multiple nutrients to estimate aspects of dietary measurement error simultaneously: with biologically relevant assumptions we will show great gains in efficiency over the common single nutrient approach. For food group data, we will develop a novel model that allows for people who never eat particular foods over a fixed time period, and we will apply this model to dietary surveillance and to prospective studies. These problems motivate a new general semiparametric likelihood framework and a new theory for model selection in this complex semiparametric framework. Semiparametric Methods: First, we are motivated by semiparametric modeling of correlated and longitudinal data, and we will develop a general framework for deriving semiparametric efficient estimates that solve problems not previously considered. We will also develop methods for semiparametric inference in measurement error problems, going far beyond what is available in the literature. The second approach arises in case-control studies with measurement error, including gene-environment interaction studies. We will consider the case that genetic and environmental factors are independent in the population, possibly after conditioning on factors to account for population stratification. We will develop methods that allow for measurement error in environmental factors, missing genotype data, analysis of haplotype data without phases and genotyping errors.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Method to Extend Research in Time (MERIT) Award (R37)
Project #
5R37CA057030-19
Application #
7229560
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Verma, Mukesh
Project Start
1992-09-01
Project End
2010-04-30
Budget Start
2007-05-01
Budget End
2008-04-30
Support Year
19
Fiscal Year
2007
Total Cost
$309,549
Indirect Cost
Name
Texas A&M University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
078592789
City
College Station
State
TX
Country
United States
Zip Code
77845
Sun, Ryan; Carroll, Raymond J; Christiani, David C et al. (2018) Testing for gene-environment interaction under exposure misspecification. Biometrics 74:653-662
Peñaranda, Augusto; Garcia, Elizabeth; Barragán, Ana M et al. (2016) Factors associated with Allergic Rhinitis in Colombian subpopulations aged 1 to 17 and 18 to 59. Rhinology 54:56-67
Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent (2016) Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures. Biostatistics 17:377-89
Kipnis, Victor; Freedman, Laurence S; Carroll, Raymond J et al. (2016) A bivariate measurement error model for semicontinuous and continuous variables: Application to nutritional epidemiology. Biometrics 72:106-15
Bhadra, Anindya; Carroll, Raymond J (2016) Exact sampling of the unobserved covariates in Bayesian spline models for measurement error problems. Stat Comput 26:827-840
Wang, Yanqing; Wang, Suojin; Carroll, Raymond J (2015) The direct integral method for confidence intervals for the ratio of two location parameters. Biometrics 71:704-13
Zhang, Xinyu; Cao, Jiguo; Carroll, Raymond J (2015) On the selection of ordinary differential equation models with application to predator-prey dynamical models. Biometrics 71:131-138
Lian, Heng; Liang, Hua; Carroll, Raymond J (2015) Variance Function Partially Linear Single-Index Models(1.) J R Stat Soc Series B Stat Methodol 77:171-194
Hong, Mee Young; Turner, Nancy D; Murphy, Mary E et al. (2015) In vivo regulation of colonic cell proliferation, differentiation, apoptosis, and P27Kip1 by dietary fish oil and butyrate in rats. Cancer Prev Res (Phila) 8:1076-83
Staicu, Ana-Maria; Lahiri, Soumen N; Carroll, Raymond J (2015) Significance tests for functional data with complex dependence structure. J Stat Plan Inference 156:1-13

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