Cohort analysis methods for occupational cancer studies

长期低水平职业暴露与高危人群关系的流行病学调查 癌症死亡率通常会遇到以下问题：1)暴露之间的潜在潜伏期影响 和疾病；2)暴露测量误差导致的潜在偏差；以及3)导致的潜在偏差 从与健康有关的就业选择中脱颖而出(即健康工人幸存者效应)。被识别的人 问题与工人保护直接相关，因为每个问题都是可能导致虚假的偏见的来源 关于职业病危害危害的结论。 该项目的目标是改进可用于解决这些问题的分析工具。我们会 对每个问题进行概念性描述，开发一个(或多个)简单的分析工具以减少或 消除潜在的偏差，通过仿真分析对所提出的分析方法进行评估，然后 利用经验数据说明了该方法的应用。我们将从探索如何使用 用于职业性癌症研究的灵活潜伏期模型。通过模拟分析，我们将评估使用 这些灵活的模型用于减少由于错误指定暴露滞后假设而产生的偏差；以及 对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在以下情况下可能出现的偏差 使用分组数据来分配暴露分数，就像在工作暴露矩阵中一样。赋值的用法 风险敞口值通常被认为会导致不产生偏向风险的伯克森误差模型 估计。利用模拟数据，我们将评估伯克森模型适用的条件， 并开发利用这一误差模型来减少偏差的方法。我们将用以下内容来说明这些方法 来自对电力公司工人的队列研究的经验数据。最后，我们将确定以下条件 在队列研究中需要哪些非标准回归方法(例如，G估计)来控制 健康的工人幸存者效应。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，指导调查人员何时使用G-估计。该提案涉及 诺拉癌症研究方法优先领域。拟议的研究结果将进一步改善 职业性癌症研究中使用的分析方法。

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.