Diet is a risk factor for many cancers. However, the appropriate latent period for dietary risk factors is unknown. A strength of the NHS database is the availability of repeat dietary information over 30+ years. One goal of this application (aim 1) is to obtain optimal methods of weighting repeated measures of diet over a 30 year period using an exponential smoothing weighting function. This approach will also be applied to non-dietary exposures such as cigarette smoking and hormone use over a long period of time. Another innovation in cancer epidemiology is the availability of multiple tumor markers which can refine the epidemiology of specific cancers according to tumor type, and help confirm the causality of an association. However, as the number of tumor markers gets large, the number of subsets of tumors also gets large.
In aim 2, we propose survival analysis methods to estimate the 2-way interaction between risk factors and tumor types as well as 3-way interactions between risk factors and combinations of tumor types. Improvements in cancer risk prediction are increasingly occurring with novel risk factors measured in case-control datasets.
Aim 3 of this application combines information on novel risk factors in case/control datasets with standard risk factors in prospective datasets to improve risk prediction. For breast cancer, an Important predictor is age at menopause. However, this is only known for women with natural menopause and bilateral oophorectomy.
In aim 4, we seek to estimate age at natural menopause among other surgical menopause women to reduce bias and enhance precision of breast cancer risk prediction.
In aim 5, we will extend the colon cancer risk prediction model developed in the previous cycle of this grant, by developing separate models for proximal cancer, distal cancer and rectal cancer. We also consider novel methods of analysis of recurrent colorectal adenoma outcome data using interval censored survival methods. This Project will interact closely with Projects 1-3 to improve our understanding of the etiology of breast, colorectal and ovarian cancers in women. It also shares with the other Projects a strong administrative and scientific infrastructure provided by Cores A (cohort follow-up and data base maintenance), B (confirmation of cancer and cause of death), C (management of the biospecimens) and D (leadership and data analysis).
This project is aimed at developing methods for refining cancer risk prediction in different ways: efficient use of time dependent covariates, estimation of risk profiles defined by tumor markers and combining risk factor information from case/control and prospective datasets.The overall goal of this project is to refine data analysis technique used in cancer epidemiology which will aid in etiology as well as in identifying women who truly are at high risk for breast, colorectal and ovarian cancer.
|Rice, Megan S; Rosner, Bernard A; Tamimi, Rulla M (2017) Percent mammographic density prediction: development of a model in the nurses' health studies. Cancer Causes Control 28:677-684|
|Hanyuda, Akiko; Cao, Yin; Hamada, Tsuyoshi et al. (2017) Body mass index and risk of colorectal carcinoma subtypes classified by tumor differentiation status. Eur J Epidemiol 32:393-407|
|Irshad, Humayun; Oh, Eun-Yeong; Schmolze, Daniel et al. (2017) Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method. Sci Rep 7:43286|
|Jiménez, M C (2017) Response to comment on plasma uric acid and risk of ischaemic stroke in women. Eur J Neurol 24:e2|
|Wang, Tiange; Huang, Tao; Heianza, Yoriko et al. (2017) Genetic Susceptibility, Change in Physical Activity, and Long-term Weight Gain. Diabetes 66:2704-2712|
|Rice, Megan S; Tworoger, Shelley S; Hankinson, Susan E et al. (2017) Breast cancer risk prediction: an update to the Rosner-Colditz breast cancer incidence model. Breast Cancer Res Treat 166:227-240|
|Linos, E; Li, W Q; Han, J et al. (2017) Lifetime ultraviolet radiation exposure and lentigo maligna melanoma. Br J Dermatol 176:1666-1668|
|Nimptsch, Katharina; Song, Mingyang; Aleksandrova, Krasimira et al. (2017) Genetic variation in the ADIPOQ gene, adiponectin concentrations and risk of colorectal cancer: a Mendelian Randomization analysis using data from three large cohort studies. Eur J Epidemiol 32:419-430|
|Masugi, Yohei; Nishihara, Reiko; Hamada, Tsuyoshi et al. (2017) Tumor PDCD1LG2 (PD-L2) Expression and the Lymphocytic Reaction to Colorectal Cancer. Cancer Immunol Res 5:1046-1055|
|Simon, Tracey G; King, Lindsay Y; Chong, Dawn Q et al. (2017) Diabetes, Metabolic Comorbidities and Risk of Hepatocellular Carcinoma: Results from Two Prospective Cohort Studies. Hepatology :|
Showing the most recent 10 out of 1577 publications