Project 4: Statistical Innovations

摘要 项目4的基本主题是开发和实施统计方法以提高癌症风险 以几种潜在的新方法预测和理解癌症病因。代谢组学的可用性 项目1、2和3的数据有可能改善对乳房、结直肠和卵巢的风险预测 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。 以及研究癌症发病率与(A)个体代谢物、(B)代谢之间的关系 途径，以及(C)代谢特征，同时使用公认的风险仔细控制风险因素 每个癌症部位的得分。Aim 2提出了一种策略来考虑结直肠癌(CRC)的筛查风险 模特们。由于筛查是结直肠癌最重要的保护因素之一，可能与 其他行为风险因素，这是高度优先的。目标3提出了一种创新的战略，将 癌症发病率和癌症死亡率的危险因素对识别无病妇女的影响 致命性癌症的高风险；这一目标是跨项目1到3的战略建模的关键，所有这些战略都建议 考虑一下癌症的存活率和致命性。目标4旨在优化使用我们独特的30年纵向 风险因素数据，以权衡随着时间的推移报告的暴露作为风险预测因素。它在标准上进行了扩展 延迟的概念，它只考虑过去几年的风险因素，使用二进制截止日期，通过允许 更平稳地对多个时间段的风险敞口进行加权。AIM 4还将在所有 项目。最后，目标5关注癌症风险因素变化的短期(或促进)影响。 发病率，同时控制长期影响。它解决了临床问题，即 短期内风险因素的变化将导致风险的相应变化。考虑到人口的老化 NHS队列和美国人口，这是高度临床优先的。总体而言，该项目将促进使用 新的和改进的统计应用程序跨越P01应用程序的项目，并在科学 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作 计划项目应用为有效实现每个项目的目标提供了实质性的附加值 同时拓展科学方法和成果。

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.