Project 4: Statistical Innovations

项目 4：统计创新

• 批准号: 8793412
• 负责人: Bernard A Rosner
• 金额: $ 15.03万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-12 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8793412
• 关键词: Accounting; Achievement; Address; Aging; Alcohol consumption; Alcohols; Behavioral; Breast Cancer Risk Factor; Cancer Biology; Cancer Etiology; Cancer Model; Cessation of life; Clinical; Colon Carcinoma; Colonoscopy; Colorectal Cancer; Communities; Computer software; Counseling; Data; Databases; Detection; Development; Diagnosis; Diet; Disease; Distal; Epidemiologist; Event; Folic Acid; Goals; High Risk Woman; Hormonal; Incidence; Individual; Label; Lasso; Life; Linear Models; Logistic Regressions; Long-Term Effects; Malignant Neoplasms; Malignant neoplasm of ovary; Metabolic; Metabolic Pathway; Methods; Modeling; Pathway interactions; Polyps; Population; Postmenopause; Probability; Regression Analysis; Reporting; Research; Resources; Risk; Risk Factors; Series; Smoking; Staging; Statistical Methods; Testing; Time; Validation; Weight; Woman; base; breast cancer diagnosis; cancer prevention; cancer risk; cancer site; clinical risk; cohort; high risk; improved; innovation; malignant breast neoplasm; metabolomics; middle age; mortality; novel; older women; predictive modeling; programs; rectal; screening; statistics

ABSTRACT The underlying theme of Project 4 is to develop and implement statistical methods to improve cancer risk prediction and to understand cancer etiology in several potentially novel ways. The availability of metabolomic data for Projects 1, 2 and 3 has the potential to improve risk prediction for breast, colorectal and ovarian cancer, and to identify new pathways underlying cancer development, yet there is also a high likelihood of discovering false positives. The analytic approaches in Aim 1 of the project address multiple comparison issues as well as look at associations between cancer incidence and (a) individual metabolites, (b) metabolic pathways, and (c) metabolic signatures, while carefully controlling for risk factors using well-established risk scores for each cancer site. Aim 2 proposes a strategy to account for screening in colorectal cancer (CRC) risk models. Since screening is one of the most important protective factors for CRC and may be associated with other behavioral risk factors, this is of high priority. Aim 3 proposes an innovative strategy for combining effects of risk factors for both cancer incidence and cancer mortality to identify disease-free women who are at high risk of lethal cancer; this aim is key to modeling strategies across Project 1 to 3, all of which propose to consider cancer survival and lethality. Aim 4 seeks to optimize the use of our unique 30 years of longitudinal risk factor data so as to weight reported exposures over time as predictors of risk. It expands on the standard concept of latency which only considers risk factors years in the past using a binary cutoff, by allowing a smoother weighting of exposures over multiple time periods. Aim 4 will also have broad utility across all the Projects. Finally, Aim 5 is concerned with short-term (or promotion) effects of changes in risk factors on cancer incidence, while simultaneously controlling for long-term effects. It addresses the clinical question of whether changes in risk factors over the short-term will result in corresponding changes in risk. Given the aging of the NHS cohort and the US population, this is of high clinical priority. Overall, this Project will promote the use of novel and improved statistical applications across the Projects of this P01 application, and in the scientific community. The close synergy between statisticians, clinicians, and epidemiologists which results from this Program Project application provides substantial added value in efficiently realizing the Aims of each Project and simultaneously expanding both scientific methods and achievements.

摘要 项目4的基本主题是开发和实施统计方法，以改善癌症风险 预测和了解癌症病因在几个潜在的新方法。代谢组学的可用性 项目1、2和3的数据有可能改善乳腺癌、结直肠癌和卵巢癌的风险预测。 癌症，并确定癌症发展的新途径，但也有很高的可能性， 发现假阳性项目目标1中的分析方法涉及多重比较 以及癌症发病率与（a）个体代谢物，（B）代谢 途径，和（c）代谢特征，同时使用公认的风险仔细控制风险因素 每个癌症部位的评分。目的2提出了一种策略，以占筛查结直肠癌（CRC）的风险 模型由于筛查是CRC最重要的保护因素之一， 其他行为风险因素，这是高优先级。目标3提出了一种创新战略， 风险因素对癌症发病率和癌症死亡率的影响，以确定无病妇女， 致命癌症的高风险;这一目标是项目1至3的建模策略的关键，所有这些都建议 考虑癌症存活率和致死率。目标4旨在优化利用我们独特的30年纵向 风险因素数据，以便随着时间的推移对报告的风险进行加权，作为风险预测因素。它扩展了标准 潜伏期的概念，它只考虑过去几年的风险因素，使用二进制截止值，通过允许 在多个时间段内更平滑地加权暴露。Aim 4还将在所有领域具有广泛的实用性， 项目最后，目标5涉及风险因素变化对癌症的短期（或促进）影响 同时控制长期影响。它解决了临床问题， 短期内风险因素的变化将导致风险的相应变化。鉴于老化的 NHS队列和美国人群，这具有高度临床优先级。总体而言，该项目将促进使用 新的和改进的统计应用程序在整个项目的P01应用程序，并在科学 社区统计学家、临床医生和流行病学家之间的密切协同作用， 计划项目的应用程序提供了大量的附加值，有效地实现每个项目的目标 同时推广科学方法和成果。