Cohort analysis methods for occupational cancer studies

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经常遇到以下问题：1)暴露与疾病之间的潜在潜伏期效应；2)暴露测量误差造成的潜在偏差；3)与健康相关的就业选择导致的潜在偏见。所确定的问题与工人保护直接相关，因为每个问题都是偏见的来源，可能导致关于职业危害不利影响的虚假结论。本研究的目标是加速创新分析工具的发展和传播，以减少偏倚，提高来自队列研究的风险估计的准确性。利用我们在初始项目中的工作，我们利用对所开发的每个问题的见解，并展示如何通过类比应用于其他研究领域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转化为我们的目的，并将它们应用于队列分析以说明它们的效用。我们将演示如何将暴露时间窗分析的标准方法与第二阶段参数延迟模型相结合，以减少偏差并提高暴露-时间-响应关联估计的精度。在最初的项目中，我们开发了一种创新的方法来直接拟合简单（单阶段）回归模型中的参数延迟模型。层次回归方法已应用于其他研究领域，用于参数函数的平滑，并显示在效果估计的准确性方面取得了显着的进步。接下来，我们将开发一种方法来正确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在暴露-疾病关联的估计中引起衰减偏差，以及贝叶斯方法如何与JEMs相结合，以提供一个直观的框架，在不引入衰减偏差的情况下处理暴露估计中的不确定性。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暴露与癌症关联的估计。在最初的项目中，我们评估了暴露-死亡率关联的偏倚，以探索标准回归方法不充分的条件。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置效应测量。利用最近在传染病流行病学中应用的结构嵌套模型的方法，我们将演示如何拟合回归模型来产生HSWE无偏倚的关联估计。我们将开发的方法产生标准效应测量，并克服了先前g方法应用的许多局限性。这项研究将改进职业性癌症研究中使用的方法。

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.