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.

项目概要/摘要 暴露和结果测量的不确定性给识别和结果带来了巨大的挑战。 例如，虽然很难很好地测量癌症的病因，但身体活动模式。 构成了许多有关电子癌症病例的病因学假设的基础。 健康记录 (EHR) 和其他行政“大数据”来源，例如医疗保险索赔数据，也 这种暴露和结果的不确定性会导致估计值出现相当大的偏差。 健康影响，掩盖了我们检测真实关联的能力，即使检测到这种关联，也可能会被低估。 测量误差和误分类校正方法的作用是有效且高效地估计 为了实现这一点，需要进行验证研究。 尽管在这个领域已经取得了很大的进展，但估计错误过程的关键特征。 多年来，当前的目标解决了具有高度科学意义的未解决问题，否则这些问题将仍然存在 如果没有这项额外的工作，我们将深入探讨癌症中出现的多方面主题。 研究，几个对于成果转化至关重要的开创性新方向 这些方法将包括基于人群的实践和政策研究的影响。 个体内部生活方式行为的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、目前对饮食和身体活动的验证研究不足 重复纸质和在线问卷自我报告以及重复浓度和回收生物标志物 获得不受一般测量误差结构影响的相对风险估计，其中可能包括 相关和有偏误差，以及估计暴露的影响，包括药物、其他临床 新方法将应用于 EHR 数据中关于癌症发病率的治疗和健康行为。 参与者内部酒精摄入量变化对美国乳腺癌发病率影响的研究 癌症协会的 CPS-II 队列和哈佛大学的护士健康研究，以及一项解开这一问题的研究 耶鲁纽黑文 Epic 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核心特征，所有这些都将附带用户友好的公开软件。 待开发的新方法 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，并通过开展大规模的 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，以及一支由具有丰富经验的癌症流行病学家组成的杰出团队 与方法团队的合作经验，指导开发及其应用 随着该项目招募有才华的初级教师和实习生，我们将解决手头的科学问题。 解决已发现的具有挑战性的问题。