New Statistical Methods for Cox Regression with Measurement Errors in Cancer and Nutritional Epidemiology

癌症和营养流行病学中具有测量误差的 Cox 回归的新统计方法

• 批准号: 10409754
• 负责人: Xin Zhou
• 金额: $ 8.38万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10409754
• 关键词: Algorithms; Attention; Calibration; Cancer and Nutrition; Computer software; Cox Models; Data; Data Analyses; Development; Diet; Dietary Assessment; Dietary intake; Eating; Epidemiologist; Epidemiology; Exposure to; Follow-Up Studies; Food; Frequencies; Health; Health Professional; Incidence; Intake; Lead; Life Style; Literature; Malignant Neoplasms; Masks; Measurement; Measures; Methodology; Methods; Modeling; Nurses; Nurses' Health Study; Nutrient; Nutritional; Outcome; Paper; Participant; Patient Self-Report; Procedures; Publishing; Questionnaires; Research; Source; Statistical Methods; Survival Analysis; Uncertainty; Woman; Work; base; cancer epidemiology; cancer risk; cohort; diet and cancer; epidemiology study; expectation; improved; interest; men; mortality; neglect; novel; nutrition; nutritional epidemiology; software development; survival outcome; user friendly software; user-friendly; validation studies

Project Summary / Abstract One of the greatest challenges in cancer and nutritional epidemiology is the uncertainty in covariate measurements, which is a major source of bias in research aimed at elucidating causal relationships between diet and cancer incidence and mortality. Dietary intake is usually estimated using a self-report food-frequency questionnaire, and this dietary assessment is often subject to substantial measurement error. The issue of exposure uncertainty lead to the potential for considerable bias in estimated health effects, masking our ability to detect true associations. Right-censored survival outcomes are common in epidemiologic practice, and the Cox regression model is typically used to model such outcomes. Regression calibration is a simple measurement error adjustment method in Cox regression, and is often used in cancer and nutritional epidemiology. However, when the degree of measurement error is large and the regression coefficient of the error-prone covariate is large, regression calibration is unsatisfactory for bias correction due to exposure uncertainty. We will develop an improved regression calibration method considering the degree of measurement error and the coefficient of the error-prone covariate simultaneously in the calibration procedure. The Expectation-Maximization (EM) algorithm has been remarkably applied for a wide variety of situations for incomplete data problems. However, EM does not receive much attention to deal with measurement error problems in survival data. We will develop a complete treatment of EM in main / validation studies to fill an important gap in the literature. User-friendly publicly available software development will be a central feature accompanying all new methods to be developed.

项目总结/摘要 癌症和营养流行病学的最大挑战之一是协变量的不确定性， 测量，这是旨在阐明因果关系的研究中偏见的主要来源， 饮食与癌症发病率和死亡率的关系。膳食摄入量通常是使用自我报告的食物频率来估计的。 调查问卷，这种饮食评估往往受到大量的测量误差。问题 暴露的不确定性导致估计的健康影响可能存在相当大的偏差，掩盖了我们的能力。 来发现真正的关联右删失生存结局在流行病学实践中很常见， 考克斯回归模型通常用于对这些结果进行建模。回归校准是一个简单的 测量误差调整方法在考克斯回归，并经常用于癌症和营养 流行病学然而，当测量误差的程度较大时， 易错协变量较大，由于暴露，回归校准对偏倚校正不满意 不确定性我们将开发一种改进的回归校准方法， 测量误差和易错协变量的系数。 期望最大化（EM）算法已经显著地应用于各种各样的情况， 数据不完整的问题。然而，EM没有得到太多的关注，以处理测量误差 生存数据的问题。我们将在主要/验证研究中开发EM的完整治疗方法， 这是文学中的一个重要空白。用户友好的公开可用软件开发将是一个核心特点 伴随着所有新方法的开发。