Cohort analysis methods for occupational cancer studies

职业癌症研究的队列分析方法

• 批准号: 8011188
• 负责人: DAVID B RICHARDSON
• 金额: $ 17.7万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8011188
• 关键词: Accounting; Address; Adverse effects; Area; Bayesian Method; Cohort Analysis; Cohort Studies; Communities; Computer software; Coupled; Data; Development; Disease; Disease Association; Dose; Employment; Epidemiologic Studies; Exposure to; Future; Goals; Health; Individual; Infectious Disease Epidemiology; Lead; Malignant Neoplasms; Measurement; Measures; Methods; Modeling; Motivation; Occupational; Occupational Epidemiology; Occupational Exposure; Occupational Malignant Neoplasm; Occupations; Paper; Positioning Attribute; Reaction Time; Regression Analysis; Research; Research Activity; Research Personnel; Risk Assessment; Risk Estimate; Sampling; Series; Solutions; Source; Staging; Survivors; Time; Translating; Uncertainty; Work; Workplace; analytical tool; anticancer research; attenuation; cancer epidemiology; cohort; evidence base; improved; innovation; insight; mortality; occupational hazard; programs; public health relevance; public health research; simulation; tool; user friendly software

DESCRIPTION (provided by applicant): Epidemiologic investigations of associations between protracted low level occupational exposures and cancer mortality routinely encounter the following problems: 1) potential latency effects between exposure and disease; 2) potential bias resulting from exposure measurement error; and, 3) potential bias resulting from health-related selection out of employment. The identified problems are of direct relevance to worker protection, as each is a source of bias that may lead to spurious conclusions about the adverse effects of occupational hazards. The goal of this research is to accelerate the development and dissemination of innovative analytical tools to reduce bias and increase precision of risk estimates derived from cohort studies. Leveraging work in our initial project, we draw upon the insights into each problem developed and demonstrate how solutions can be found via analogies to state-of-the-art methods applied in other research areas. We can quickly translate these methods for application to our purposes and apply them in cohort analyses to illustrate their utility. We will demonstrate how the standard approach to exposure time-window analysis may be coupled with a second stage parametric latency model to reduce bias and improve precision of estimates of exposure-time-response associations. During the initial project we developed an innovative method to directly fit a parametric latency model in a simple (single stage) regression model. Hierarchical regression methods have been applied in other research areas for smoothing of parametric functions and shown to yield notable gains in the accuracy of effect estimates. Next, we will develop an approach to correctly account for uncertainty in exposure estimates derived via a job-exposure-matrix (JEM). Recently, investigators have used exposure simulation approaches to generate 'realizations' of exposure scores sampled from underlying distributions. We will demonstrate that the exposure simulation approach may induce attenuation bias in estimates of exposure-disease associations and how Bayesian methods may be coupled with JEMs to provide an intuitive framework for handling uncertainty in exposure estimates without introducing attenuation bias. Finally, we will develop a readily-implementable approach for fitting structural nested models that provide estimates of occupational exposure-cancer associations that are not biased by the healthy worker survivor effect. During the initial project, we assessed bias in exposure-mortality associations in order to explore conditions under which standard regression methods are inadequate. This work showed that there are many settings in which standard regression analysis yielded strongly biased effect measures. Drawing upon methods for structural nested models recently applied in infectious disease epidemiology, we will demonstrate how regression models can be fitted to produce estimates of association that are unbiased by the HSWE. The approach that we will develop produces standard effect measures and overcomes many limitations of prior applications of G-methods. This research will improve the methods used in occupational cancer studies. PUBLIC HEALTH RELEVANCE: The goal of the proposed research, which is a competing continuation of R01-CA117841 Cohort Analysis Methods for Occupational Cancer Studies, is to accelerate the development and dissemination of innovative analytical tools to improve the analysis of occupational cohort studies of cancer.

描述（由申请人提供）：长期低水平职业暴露与癌症死亡率之间关系的流行病学调查通常会遇到以下问题：1）暴露与疾病之间的潜在潜伏效应; 2）暴露测量误差导致的潜在偏倚;以及3）与健康有关的就业选择导致的潜在偏倚。已查明的问题与工人保护直接相关，因为每一个问题都是偏见的来源，可能导致对职业危害的不利影响得出错误的结论。这项研究的目标是加快创新分析工具的开发和推广，以减少偏倚，提高队列研究得出的风险估计的准确性。利用我们最初项目中的工作，我们借鉴了对每个问题的见解，并展示了如何通过类比其他研究领域中应用的最先进方法来找到解决方案。我们可以快速地将这些方法应用于我们的目的，并将其应用于队列分析，以说明其实用性。我们将演示如何暴露时间窗分析的标准方法可以与第二阶段参数潜伏期模型相结合，以减少偏差，提高估计的准确性，时间响应协会。在最初的项目中，我们开发了一种创新的方法，可以在简单的（单阶段）回归模型中直接拟合参数延迟模型。分层回归方法已被应用于其他研究领域的平滑参数函数，并显示出显着的收益效果估计的准确性。接下来，我们将开发一种方法来正确地解释通过工作暴露矩阵（JEM）得出的暴露估计中的不确定性。最近，研究人员使用暴露模拟方法来生成从底层分布中采样的暴露分数的“实现”。我们将证明，暴露模拟方法可能会引起衰减偏差估计的疾病的关联，以及如何贝叶斯方法可以与JEM相结合，提供一个直观的框架，处理暴露估计的不确定性，而不引入衰减偏差。最后，我们将开发一种易于实施的方法来拟合结构嵌套模型，该模型提供了不受健康工人幸存者效应影响的职业性死亡-癌症关联的估计。在最初的项目中，我们评估了死亡率-死亡率相关性的偏倚，以探索标准回归方法不适用的条件。这项工作表明，在许多情况下，标准回归分析产生了强烈的偏倚效应。借鉴最近应用于传染病流行病学的结构嵌套模型的方法，我们将演示如何回归模型可以拟合产生的关联估计是无偏的HSWE。我们将开发的方法产生标准的效果措施，并克服了许多限制以前的应用G-方法。这项研究将改进职业性癌症研究的方法。 公共卫生相关性：该研究是R 01-CA 117841职业癌症研究队列分析方法的竞争性延续，旨在加速创新分析工具的开发和传播，以改善癌症职业队列研究的分析。