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）与健康相关的就业选择导致的潜在偏倚（即，健康工人幸存者效应）。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。该项目的目标是改进现有的分析工具，以解决这些问题。我们将开发每个问题的概念描述，开发一个简单的分析工具（或工具），以减少或消除潜在的偏见，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的方法的应用。我们将开始探索使用灵活的潜伏期模型的职业癌症研究。通过模拟分析，我们将评估使用这些灵活的模型，以减少由于暴露滞后假设的错误规范的偏差，并在橡胶盐酸盐和石棉纺织工人队列数据的实证分析，我们将说明这些方法的应用。接下来，我们将开发一种方法来控制当分组数据用于分配暴露分数时可能出现的偏差，例如在工作暴露矩阵中。使用指定的暴露值通常被认为会导致Berkson误差模型，该模型不会产生有偏的风险估计。使用模拟数据，我们将评估Berkson模型适用的条件，并开发利用此误差模型来减少偏差的方法。我们将说明这些方法与实证数据从电力公用事业工人的队列研究。最后，我们将确定非标准回归方法（例如，G-估计）是必要的队列研究，以控制健康工人的幸存者的影响。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。该提案涉及诺拉关于癌症研究方法的优先领域。该研究结果将进一步完善职业性癌症研究中的分析方法。

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.