New Epidemiologic Methods for Reducing Measurement Error and Misclassification Bias in Cancer Epidemiology

项目摘要/摘要 暴露和结果衡量方面的不确定性对确定和 对致癌原因进行量化。例如，尽管很难很好地衡量，但体力活动模式 构成了许多关于癌症风险的病因学假说的基础。在电子系统中发现的癌症病例 健康记录(EHR)和其他管理大数据来源，如联邦医疗保险索赔数据，也是 容易被错误分类的。这种暴露和结果的不确定性导致了对估计的相当大的偏差 对健康的影响，掩盖了我们检测真实关联的能力，如果检测到的话，很可能被低估了。 测量误差和错误分类校正方法的作用是有效和高效地估计 暴露与癌症结局之间的关系。要做到这一点，需要进行验证研究 估计错误过程的关键特征。尽管在这一领域已经取得了许多成就，但 几年来，目前的目标是解决尚未解决的具有高度科学意义的问题，否则这些问题将继续存在 如果没有这项额外的工作，就没有答复。我们将深入探讨癌症中出现的多个方面的主题 研究，处理对翻译结果至关重要的几个开创性的新方向 以人口为基础的研究实践和政策。这些方法将包括对以下影响的估计 个人内生活方式改变对癌症风险的影响修正了改变中的测量误差 变量，利用复杂的、目前未得到充分利用的饮食和体力活动验证研究，包括 重复的纸质和在线问卷自我报告以及重复的浓度和回收生物标志物 要获得不受一般测量误差结构影响的相对风险估计，可能包括 相关和有偏见的错误，以及估计暴露的影响，包括药物，其他临床 在EHR数据中，治疗和健康行为对癌症发病率的影响。新方法将应用于 美国参与者酒精摄入量变化对乳腺癌发病率影响的研究 癌症协会的CPS-II队列和哈佛护士的健康研究，以及一项解开 耶鲁大学纽黑文史诗EHR中糖尿病和糖尿病药物对结直肠癌风险的影响。 传播是这项研究的一个中心特征。用户友好的公开可用的软件将与所有 有待开发的新方法。新方法将通过短期课程和讲座在 国家和国际流行病学和统计会议，并通过制定大规模的 在线开放课程(MOOC)。我们组建了一支优秀的测量误差专家团队 方法和统计理论，以及一支杰出的癌症流行病学家团队 与方法团队的协作经验，以指导开发及其应用到 手头的科学问题。随着有才华的初级教员和实习生被招募到这个项目中，我们将 解决已确定的具有挑战性的问题。

Project Summary/Abstract Uncertainty in exposure and outcome measurements poses substantial challenges to the identification and quantification of the causes of cancer. For example, although difficult to measure well, physical activity patterns form the basis of many etiologic hypotheses concerning cancer risk. Cancer cases identified in electronic health records (EHR) and other administrative ‘big data’ sources, such as Medicare claims data, are also subject to misclassification. This exposure and outcome uncertainty leads to considerable bias in estimated health effects, masking our ability to detect true associations, which are likely underestimated if detected at all. It is the role of measurement error and misclassification correction methods to validly and efficiently estimate the relationship between exposures and cancer outcomes. To accomplish this, a validation study is required for estimating key features of the error process. Although much has been accomplished in this domain over the years, the current aims address unsolved problems of high scientific significance that would otherwise remain unanswered without this additional work. We will drill down into the multi-faceted themes that arise in cancer research, tackling several seminal new directions of critical importance for the translation of the results of population-based research to practice and policy. These methods will include estimation of the effects of within-individual change in lifestyle behaviors on cancer risk corrected for measurement error in the change variables, utilizing complex, currently under-accessed validation studies of diet and physical activity comprised of repeated paper and online questionnaire self-reports and repeated concentration and recovery biomarkers to obtain relative risk estimates unbiased by general measurement error structures which may include correlated and biased errors, and estimating effects of exposures, including medications, other clinical treatments, and health behaviors, on cancer incidence in EHR data. The new methods will be applied to studies of the impact of within-participant change in alcohol intake on breast cancer incidence in the American Cancer Society’s CPS-II cohort and in Harvard’s Nurses’ Health Study, and to a study disentangling the impacts of diabetes and diabetes medications on colorectal cancer risk in Yale New Haven’s Epic EHRs. Dissemination is a central feature of this research. User-friendly publicly available software will accompany all new methods to be developed. The new methods will be disseminated through short courses and lectures at national and international epidemiologic and statistical conferences, and through the development of a massive online open course (MOOC). We have assembled an outstanding team of experts in measurement error methods and statistical theory, along with an exceptional team of cancer epidemiologists with much prior collaborative experience with the methods team, to guide the developments and their applications to the scientific problems at hand. With the talented junior faculty and trainees to be recruited for this project, we will solve the challenging problems that have been identified.