Cohort analysis methods for occupational cancer studies

职业性长期低水平暴露与放射性疾病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效应 2）暴露测量误差导致的潜在偏倚;以及3） 从与健康有关的就业选择（即，健康工人幸存者效应）。所识别的 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，可能导致虚假的 关于职业危害的不利影响的结论。 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 对每个问题进行概念性描述，开发一个简单的分析工具（或多个工具）， 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的方法的应用。我们将开始探索使用 灵活的潜伏期模型用于职业性癌症研究。通过模拟分析，我们将评估 这些灵活的模型，用于减少由于暴露滞后假设的错误说明而产生的偏差;以及， 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可能出现的偏差， 分组数据用于分配暴露分数，如在工作暴露矩阵中。分配的使用 暴露值通常被假设为导致不产生偏差风险的Berkson误差模型 估算使用模拟数据，我们将评估Berkson模型适用的条件， 并开发利用这种误差模型来减少偏差的方法。我们将用以下示例来说明这些方法： 来自电力公用事业工人队列研究的经验数据。最后，我们将确定条件下， 哪些非标准回归方法（例如，G估计）在队列研究中是必要的，以控制 健康工人幸存者效应。我们将使用模拟方法来探索这些条件， 简单的分析工具，以指导研究人员何时使用G估计。该提案涉及 诺拉癌症研究方法的优先领域。建议的研究结果将进一步提高 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.