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

减少癌症流行病学中测量误差和误分类偏差的新流行病学方法

• 批准号: 10801058
• 负责人: DONNA L SPIEGELMAN
• 金额: $ 75万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10801058
• 关键词: Acceleration; Address; Adult; Alcohol consumption; American Cancer Society; Behavior; Big Data; Biological Markers; Birth; Cancer Etiology; Cancer Prevention Study II; Child; Clinical Treatment; Cohort Studies; Colorectal Cancer; Communities; Complex; Computer software; Data; Development; Diabetes Mellitus; Diet; Disease; Electronic Health Record; Energy Intake; Epidemiologic Methods; Epidemiologist; Epidemiology; Etiology; Exposure to; Faculty; Hand; Health; Health behavior; Heterogeneity; Hospitals; Incidence; Individual; Infant; International; Investments; Life Style; Malignant Neoplasms; Manuals; Measurement; Measures; Medicare claim; Methodology; Methods; National Cancer Institute; Negative Finding; Non-Insulin-Dependent Diabetes Mellitus; Nurses' Health Study; Nutritional; Outcome; Paper; Participant; Patient Self-Report; Pattern; Pharmaceutical Preparations; Physical activity; Policies; Population Research; Prevalence; Procedures; Process; Public Health; Questionnaires; Recovery; Relative Risks; Research; Risk Estimate; Risk Factors; Role; Seminal; Sleep; Source; Structure; Talents; Time; Translations; Uncertainty; United States National Institutes of Health; Validation; Variant; Woman; Work; anticancer research; attenuation; cancer epidemiology; cancer prevention; cancer risk; clinical practice; cohort; colorectal cancer risk; comorbidity; course development; design; dietary; energy balance; epidemiology study; experience; follow-up; improved; innovation; interest; lectures; malignant breast neoplasm; massive open online courses; men; method development; mortality; novel; nutrition; prospective; recruit; research to practice; sedentary lifestyle; software development; statistics; symposium; theories; user friendly software; user-friendly; validation studies; web site; webinar

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.

项目总结/摘要 暴露和结果测量的不确定性对确定和 量化癌症的原因。例如，尽管很难很好地测量， 形成了许多关于癌症风险的病因学假设的基础。癌症个案电子化 健康记录（EHR）和其他行政“大数据”来源，如医疗保险索赔数据，也是 容易被误分类。这种暴露和结果的不确定性导致了估计的相当大的偏差。 健康影响，掩盖了我们检测真正关联的能力，即使检测到，也可能被低估。 测量误差和误分类校正方法的作用是有效和高效地估计 暴露与癌症结果之间的关系。为此，需要进行验证研究， 估计误差过程的关键特征。尽管在这一领域取得了很大成就， 多年来，目前的目标解决了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 如果没有这些额外的工作就没有答案。我们将深入探讨癌症中出现的多方面主题 研究，解决几个开创性的新方向的翻译的结果至关重要， 以人口为基础的研究到实践和政策。这些方法将包括估计 生活方式行为的个体内变化对癌症风险的影响，校正了变化中的测量误差 变量，利用复杂的，目前尚未获得的饮食和身体活动的验证研究，包括 重复的纸质和在线问卷自我报告以及重复的浓度和恢复生物标志物 为了获得相对风险估计，一般测量误差结构可能包括 相关和偏倚误差，以及估计暴露的影响，包括药物、其他临床 治疗和健康行为对EHR数据中癌症发病率的影响。新方法将应用于 研究参与者内酒精摄入量变化对美国乳腺癌发病率的影响 癌症协会的CPS-II队列和哈佛的护士健康研究，以及一项研究， 糖尿病和糖尿病药物对结直肠癌风险的影响，耶鲁纽黑文的史诗EHR。 传播是这项研究的一个中心特征。用户友好的公开软件将伴随所有 新方法有待开发。新方法将通过短期课程和讲座进行传播， 国家和国际流行病学和统计会议，并通过发展大规模的 在线开放课程（MOOC）。我们在测量误差方面聚集了一支优秀的专家团队 方法和统计理论，沿着一个特殊的癌症流行病学家团队， 与方法团队的合作经验，以指导开发及其应用， 手头的科学问题。与有才华的初级教师和学员将被招募为这个项目，我们将 解决已确定的挑战性问题。