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.

项目摘要/摘要 暴露和结果测量的不确定性对识别和 癌症原因的数量。例如，尽管很难很好地测量身体活动模式 构成许多关于癌症风险的病因学假设的基础。电子中确定的癌症病例 健康记录（EHR）和其他行政“大数据”来源（例如Medicare Soppors数据）也是 可能会误分类。这种暴露和结果不确定性导致估计的偏见 健康效应，掩盖了我们检测真正关联的能力，如果发现的话，这可能会被低估。 它是测量误差和错误分类校正方法的作用，可有效有效地估计 暴露与癌症结局之间的关系。为此，需要进行验证研究 估计错误过程的关键特征。尽管在这个领域已经完成了很多 几年，目前的目的解决了尚未解决高科学意义的问题，否则 没有这项额外工作的未解决。我们将深入研究癌症中出现的多方面主题 研究，针对转换结果的几个新的第二个新指导 基于人群的实践和政策研究。这些方法将包括估计 在癌症风险上的生活方式行为的个人内部变化纠正了变化的测量误差 使用复杂的，当前对饮食和体育锻炼的验证研究的变量已完成 重复的纸张和在线问卷的自我报告以及重复集中和恢复生物标志物的 要获得一般测量误差结构的相对风险估计，可能包括 相关和偏见的错误以及估计暴露的影响，包括药物，其他临床 EHR数据中癌症发病率的治疗和健康行为。新方法将应用于 研究参与性内变化对酒精摄入量对美国乳腺癌发病率的影响 癌症协会的CPS-II队列和哈佛护士的健康研究，并研究了一项研究 耶鲁纽黑文的Epic EHR中糖尿病和糖尿病药物对结直肠癌风险的影响。 传播是这项研究的主要特征。用户友好的公开软件将容纳所有人 要开发的新方法。新方法将通过简短的课程和讲座来传播 国家和国际流行病学和统计会议，以及大规模发展 在线开放课程（MOOC）。我们组建了一个杰出的测量专家团队 方法和统计理论，以及一个卓越的癌症流行病学家，很重要 与方法团队的协作经验，指导发展及其应用程序 手头的科学问题。随着才华横溢的初级教师和学员将被招募为这个项目，我们将 解决已确定的挑战问题。