This proposal reflects our continuing interest in the development of new statistical methods, and their application to problems related to diet, physical activity and cancer. The projects mentioned here will be undertaken with NCI scientists specializing in nutrition, physical activity and statistics. The P.I. has visited the NCI on many occasions, including 3 sabbaticals, the most recent being in 2013-2014. An important outgrowth of these continuing relationships is that we have access to a large number of unique data sets, the analysis of which will guide our research program. Locally, besides the P.I., we have assembled a team that has experts in a wide variety of statistical methodology relevant to diet, physical activity and cancer outcomes.
In specific aim 1 we will develop new models and methods for the analysis of dietary patterns, a field of major importance in nutrition. This will include analyzing many dietary components simultaneously, estimating from short term instruments the number of people who do not consume certain dietary components, e.g., alcoholic beverages, and building new dietary indices that combine adherence to dietary guidelines with power to predict the risk of cancer and other health outcomes.
In specific aim 2, we explain that the assessment of dietary intakes and physical activity is being revolutionized by new technologies, including web-based instruments, increasing use of accelerometers, longer-term studies with repeated dietary assessment, and biomarker studies. To take advantage of these new data structures, we will propose a series of projects including physical activity pattern analysis and risk, within and between individual variance function esti- mation, modeling of time-varying usual dietary intake and physical activity with interactions, and misclassification and measurement error, and latent variable models.
In specific aim 3, we will develop new methods for the analysis of case-control studies, including the analysis of gene-environment interactions on risk, efficient semi parametric regression in the analysis of secondary outcomes.
Patterns of dietary intakes and patterns of physical activity in a population are important for population surveillance, since they are consistently found to be associated with the risk of cancer and other health outcomes. Emerging new technologies for measuring dietary intake and physical activity will allow a deeper understanding of usual longer-term intakes/activities, allowing correction for biases and measurement errors inherent in current self-report instruments. Working closely with NCI nutritionists, physical activity specialists and statisticians, we have developed an ambitious agenda to develop new statistical methods to analyze these patterns and model their effects on cancer and other health outcomes, methods that will become state of the art in these fields. We also have an agenda for the analysis of case-control studies incorporating gene- environment interactions and secondary analysis of such studies.
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| Liang, Liang; Carroll, Raymond; Ma, Yanyuan (2018) Dimension reduction and estimation in the secondary analysis of case-control studies. Electron J Stat 12:1782-1821 |
| Matthews, Charles E; Kozey Keadle, Sarah; Moore, Steven C et al. (2018) Measurement of Active and Sedentary Behavior in Context of Large Epidemiologic Studies. Med Sci Sports Exerc 50:266-276 |
| Li, Haocheng; Staudenmayer, John; Wang, Tianying et al. (2018) Three-part joint modeling methods for complex functional data mixed with zero-and-one-inflated proportions and zero-inflated continuous outcomes with skewness. Stat Med 37:611-626 |
| Sarkar, Abhra; Pati, Debdeep; Chakraborty, Antik et al. (2018) Bayesian Semiparametric Multivariate Density Deconvolution. J Am Stat Assoc 113:401-416 |
| Sun, Ryan; Carroll, Raymond J; Christiani, David C et al. (2018) Testing for gene-environment interaction under exposure misspecification. Biometrics 74:653-662 |
| Tekwe, Carmen D; Zoh, Roger S; Bazer, Fuller W et al. (2018) Functional multiple indicators, multiple causes measurement error models. Biometrics 74:127-134 |
| Kim, Janet S; Staicu, Ana-Maria; Maity, Arnab et al. (2018) Additive Function-on-Function Regression. J Comput Graph Stat 27:234-244 |
| Cook, Scott J; Blas, Betsabe; Carroll, Raymond J et al. (2017) Two Wrongs Make a Right: Addressing Underreporting in Binary Data from Multiple Sources. Polit Anal 25:223-240 |
| Stalder, Odile; Asher, Alex; Liang, Liang et al. (2017) Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies. Biometrika 104:801-812 |
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