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.

抽象的 项目 4 的基本主题是开发和实施统计方法以降低癌症风险 以几种潜在的新颖方式预测和了解癌症病因。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 发现误报。该项目目标 1 中的分析方法解决了多重比较问题 问题并研究癌症发病率与 (a) 个体代谢物、(b) 代谢之间的关联 途径，以及（c）代谢特征，同时使用既定的风险仔细控制风险因素 每个癌症部位的分数。目标 2 提出了一种考虑结直肠癌 (CRC) 风险筛查的策略 模型。由于筛查是 CRC 最重要的保护因素之一，并且可能与 其他行为风险因素，这是高度优先的。目标 3 提出了一项创新战略，将 风险因素对癌症发病率和癌症死亡率的影响，以确定处于以下状态的无病妇女 致命癌症的高风险；这一目标是项目 1 至项目 3 建模策略的关键，所有这些都建议 考虑癌症的生存率和致死率。目标 4 寻求优化我们独特的 30 年纵向数据的使用 风险因素数据，以便对一段时间内报告的暴露情况进行加权，作为风险的预测因子。它扩展了标准 延迟的概念，仅考虑过去几年的风险因素，使用二进制截止，通过允许 多个时间段内风险的权重更加平滑。目标 4 还将在所有领域具有广泛的效用 项目。最后，目标 5 涉及危险因素变化对癌症的短期（或促进）影响 发生率，同时控制长期影响。它解决了以下临床问题： 短期内风险因素的变化将导致风险相应变化。鉴于老龄化 对于 NHS 队列和美国人口来说，这是临床高度重视的。总体而言，该项目将促进使用 该 P01 应用程序的各个项目以及科学领域中新颖且改进的统计应用程序 社区。由此产生的统计学家、临床医生和流行病学家之间的密切协同作用 项目申请为有效实现每个项目的目标提供了巨大的附加值 同时扩大科学方法和成果。