Methodologic Innovations in Cancer Epidemiology

项目摘要 癌症的发展需要很多年的时间，并且由于癌症而导致的死亡可能在诊断后很多年发生。 因此，重要的是要使用创新的分析方法，以最佳利用所有曝光数据 在诊断之前和之后。我们在当前的应用中提出了四种类型的创新。 致命的癌症模型：最高的公共卫生利益是什么风险因素使无病 将来可能死于某种癌症。我们以前开发过致命的模型 通过将癌症发病率模型与癌症诊断后的预后模型相结合来预测癌症。在 在本申请中，我们建议在结直肠癌（CRC）的情况下扩展这种方法， 考虑致癌过程的三个阶段：（a）晚期息肉的发展，（B） 在晚期息肉被识别并切除后发生CRC事件，以及（c）由于 在发生CRC的患者中。 潜伏期模型：一些前瞻性研究在几个时间点提供了风险因素数据。一 问题是如何最佳地利用这些数据来预测癌症发病率。一种方法是使用 最近的暴露;第二种方法是使用暴露的总持续时间;第三种方法是 在暴露和结果评估之间引入滞后。在本申请中，我们提出了一种延迟 模型来估计先前暴露的最佳权重，以预测癌症发病率;这些 模型增强了对特定风险因素的生物机制的理解。 治愈模型：已经有许多关于预测癌症患者死亡率的风险因素的研究。 然而，对于某些癌症，如果患者在给定的时间段内没有死于癌症（例如， 5年内的CRC），那么他们不太可能死于癌症， 到另一个原因（即，已治愈）。但是预测治愈的风险因素是什么呢？虽然治愈 虽然以前使用过模型，但它们大多基于诊断后的风险因素。据我们所知， 这是第一个考虑诊断前风险因素作为治愈预测因素的建议。 评估筛查对CRC风险模型的影响：结肠镜检查是CRC的现行标准 筛选它是独特的，因为如果癌前病变（即，他们被发现，他们被发现。 移除，并且CRC进展的自然史被中断。然而，即使这些病变是 对于某些受试者，这些病变在未来更有可能再次发展。照经上所 挑战如何控制CRC风险模型中筛查的影响。在本申请中，我们提出了一种 通过评估风险因素对腺瘤的影响来控制筛查的创新方法 发病率，以及腺瘤发病率对CRC风险的影响。

Project Summary Cancers take many years to develop, and death due to a cancer may occur many years after diagnosis. Therefore, it is important to use innovative methods of analysis to make optimal use of all exposure data both before and after diagnosis. We propose four types of innovations in the current application. Lethal cancer models: Of highest public health interest is what risk factors predispose a disease-free subject to die due to a specific cancer in the future. We have previously developed models for lethal cancer by integrating models for cancer incidence with models for prognosis after a cancer diagnosis. In this application, we propose to extend this approach in the case of colorectal cancer (CRC) by considering three stages in the carcinogenic process: (a) development of advanced polyp, (b) development of incident CRC after advanced polyp have been identified and removed, and (c) death due to CRC among patients with incident CRC. Latency models: Some prospective studies have risk factor data available at several points in time. One issue is how these data should be optimally used to predict cancer incidence. One approach is to use the most recent exposure; a 2nd approach is to use the total duration of exposure; a 3rd approach is to introduce a lag between exposure and outcome assessment. In this application, we propose a latency model to estimate the optimal weighting of previous exposures to predict cancer incidence; these models enhance understanding of biological mechanisms for specific risk factors. Cure Models: There have been many studies of risk factors predicting mortality among cancer patients. However, for some cancers, if patients do not die from their cancer over a given period of time (e.g., within 5 years for CRC), then they are unlikely to ever die due to their cancer, and will probably die due to another cause (i.e., they are cured). But what are the risk factors that predict cure? Although cure models have been used before, they mostly are based on post-diagnostic risk factors. To our knowledge, this is the 1st proposal to consider pre-diagnostic risk factors as predictors of cure. Assessing Effects of Screening on CRC risk models: Colonoscopy is the current standard for CRC screening. It is unique, in that if pre-cancerous lesions (i.e., adenomas) are found, then they are removed, and the natural history of CRC progression is interrupted. However, even if these lesions are removed, for some subjects, these lesions are more likely to develop again at a future time. Thus, it is challenging how to control for effects of screening in CRC risk models. In this application, we propose an innovative approach to control for screening by both assessing effects of a risk factor on adenoma incidence, and effects of adenoma incidence on CRC risk.