G-estimation methods and applications to quantitative exposure

G 估计方法及其在定量曝光中的应用

• 批准号: 8227756
• 负责人: Sally Picciotto
• 金额: $ 7.75万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8227756
• 关键词: estimation; methods; applications; quantitative; exposure

DESCRIPTION (provided by applicant): When workers with poor health decrease their exposure but healthy workers do not, it becomes difficult to detect an association even when exposure causes disease. This healthy worker survivor effect is a well known and ubiquitous bias that affects occupational studies of a wide range of health outcomes and exposures. It is particularly problematic in studies of long-term exposures and chronic diseases. There are no satisfactory solutions to the healthy worker survivor effect using conventional methods for analyzing longitudinal occupational data. Fortunately, alternative methods have been developed that do eliminate this type of bias. G-estimation of structural nested accelerated failure time models is one such technique. It unlinks observed exposure from pre-hire prognosis within strata of prior exposure and covariates, thereby ensuring that the healthy worker survivor effect does not bias results. The parameter obtained represents the natural log of the factor by which one year of exposure decreases survival time. Over the past 15 years, several authors have encouraged the adoption of this method, but without success. Very recently, in the first application of g-estimation analysis in an occupational study, longer duration of exposure to oil-based metalworking fluids was clearly related to decreased time to heart disease mortality. This evidence from the UAW-GM autoworkers cohort is the first in the literature for this effect. The importance of this new method is underscored by the failure of conventional analyses to detect this association. Methods for controlling the healthy worker survivor effect and similar biases (known more generally as "causal methods") have up to now focused on binary annual exposure measures. If workers are exposed at various levels, higher exposures may be more likely to cause disease, or may cause disease sooner. Unlike binary exposure measures that yield estimates for the effect of exposure duration, quantitative measures can distinguish etiologically relevant levels of exposure. Quantitative exposure-response characterization is also necessary for risk assessment providing guidance for policy. This project therefore aims to extend causal methods in order to evaluate, without healthy worker survivor bias, the effect of total quantitative exposure on survival time. These new methods will then be applied to the UAW-GM autoworkers cohort to explore the causal effect of quantitative exposure to oil-based metalworking fluids on cardiovascular outcomes. Successful application of g-estimation to total exposures in this case would be a major breakthrough that could pave the way for other occupational studies to handle the healthy worker survivor effect correctly while considering quantitative exposure rather than duration of exposure. PUBLIC HEALTH RELEVANCE: When workers in poorer health are more likely to decrease their exposure by transferring to unexposed jobs or leaving work entirely, it becomes difficult to detect an association even if exposure causes disease. This project aims to improve control of this "healthy worker survivor effect" by extending causal methods (hitherto applied only to binary exposures) to cumulative exposures, in order to investigate the health impact of interventions to limit occupational exposures. The method will be applied in the UAW-GM autoworkers cohort to study the effects of cumulative exposure to oil-based metalworking fluids on cardiovascular outcomes.

描述（由申请人提供）：当健康状况不佳的工人减少接触，而健康工人没有减少接触时，即使接触导致疾病，也很难检测出相关性。这种健康的工人幸存者效应是一个众所周知的和普遍存在的偏见，影响了广泛的健康结果和暴露的职业研究。在长期接触和慢性疾病的研究中，这一点尤其成问题。使用传统的方法分析纵向职业数据，没有令人满意的解决方案的健康工人的幸存者效应。幸运的是，替代方法已经开发出来，消除了这种类型的偏见。结构嵌套加速失效时间模型的G-估计就是这样一种技术。它将观察到的暴露与先前暴露层和协变量内的雇用前预后联系起来，从而确保健康工人幸存者效应不会使结果产生偏差。所获得的参数代表一年暴露减少生存时间的因子的自然对数。 在过去的15年里，一些作者鼓励采用这种方法，但没有成功。最近，在一项职业研究中首次应用g估计分析，暴露于油基金属加工液的时间较长，与心脏病死亡率降低明显相关。来自UAW-GM汽车工人队列的这一证据是文献中首次出现这种效应。这种新方法的重要性是强调了传统的分析，以检测这种关联的失败。 控制健康工人幸存者效应和类似偏差的方法（更一般地称为“因果方法”）迄今为止一直专注于二进制年度暴露措施。如果工人受到不同程度的暴露，较高的暴露可能更有可能导致疾病，或可能更早导致疾病。与估计暴露持续时间影响的二进制暴露措施不同，定量措施可以区分病因相关的暴露水平。定量的风险-反应特征描述对于风险评估也是必要的，为政策提供指导。因此，该项目旨在扩展因果关系的方法，以评估，没有健康的工人幸存者的偏见，对生存时间的总定量暴露的影响。然后，这些新方法将应用于UAW-GM汽车工人队列，以探索定量暴露于油基金属加工液对心血管结局的因果关系。在这种情况下，成功地应用g-估计总暴露量将是一个重大突破，可以为其他职业研究铺平道路，正确处理健康工人幸存者的影响，同时考虑定量暴露，而不是暴露的持续时间。 公共卫生相关性：当健康状况较差的工人更有可能通过转移到未暴露的工作或完全离开工作来减少接触时，即使接触会导致疾病，也很难检测出两者之间的关联。该项目旨在通过将因果方法（迄今仅适用于二元暴露）扩展到累积暴露，以调查限制职业暴露的干预措施对健康的影响，从而改善对这种“健康工人幸存者效应”的控制。该方法将应用于UAW-GM汽车工人队列，以研究累积暴露于油基金属加工液对心血管结局的影响。