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)与健康相关的选择导致的潜在偏见（即健康工人幸存者效应）。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。这个项目的目标是改进可用的分析工具来解决这些问题。我们将对每个问题进行概念性描述，开发一个（或多个）简单的分析工具来减少或消除潜在的偏差，通过模拟分析评估所提出的分析方法，然后使用经验数据说明所提出方法的应用。我们将从探索灵活潜伏期模型在职业性癌症研究中的应用开始。通过模拟分析，我们将评估这些灵活模型的使用，以减少由于暴露滞后假设的错误规范而导致的偏差；并在盐酸盐橡胶和石棉纺织工人队列数据的实证分析中，说明了这些方法的应用。接下来，我们将开发一种方法来控制当使用分组数据来分配暴露分数时可能出现的偏差，如在工作暴露矩阵中。通常假定使用指定的暴露值会产生伯克森误差模型，该模型不会产生有偏差的风险估计。使用模拟数据，我们将评估Berkson模型适用的条件，并开发利用该误差模型来减少偏差的方法。我们将用来自电力公司工人队列研究的经验数据来说明这些方法。最后，我们将确定在队列研究中需要非标准回归方法（例如g估计）来控制健康工人幸存者效应的条件。我们将使用模拟方法来探索这些条件，并开发简单的分析工具来指导研究人员何时使用g估计。该提案涉及NORA在癌症研究方法方面的优先领域。本研究结果将进一步改进职业性癌症研究中使用的分析方法。

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 (i.e., the healthy worker survivor effect). 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 project is to improve the analytical tools available to address these problems. We will develop a conceptual description of each problem, develop a simple analytical tool (or tools) to reduce or eliminate the potential bias, evaluate the proposed analytical method via simulation analyses, and then illustrate the application of the proposed method using empirical data. We will begin by exploring the use of flexible latency models for occupational cancer studies. Via simulation analyses, we will evaluate the use of these flexible models for reducing bias due to mis-specification of exposure lag assumptions; and, in empirical analyses of rubber hydrochloride and asbestos textile worker cohort data, we will illustrate the application of these methods. Next, we will develop an approach to control for bias that can arise when grouped data are used to assign exposure scores, as in a job-exposure matrix. The use of assigned exposure values is often assumed to result in a Berkson error model that does not produce biased risk estimates. Using simulated data, we will evaluate the conditions under which the Berkson model applies, and develop approaches to exploit this error model to reduce bias. We will illustrate these approaches with empirical data from a cohort study of electrical utility workers. Finally, we will identify the conditions under which non-standard regression methods (e.g., G-estimation) are necessary in cohort studies to control for the healthy worker survivor effect. We will use simulation methods to explore these conditions, and develop simple analytical tools to guide investigators on when to use G-estimation. The proposal addresses the NORA priority area on cancer research methods. The results of the proposed research will further improve the analytic methods used in occupational cancer studies.