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Evaluation of a method to indirectly adjust for unmeasured covariates in the association between fine particulate matter and mortality

机译：对细颗粒物质与死亡率之间关联中的未测量协变量的间接调节方法的评价

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Background: Indirect adjustment via partitioned regression is a promising technique to control for unmeasured confounding in large epidemiological studies. The method uses a representative ancillary dataset to estimate the association between variables missing in a primary dataset with the complete set of variables of the ancillary dataset to produce an adjusted risk estimate for the variable in question. The objective of this paper is threefold: 1) evaluate the method for non-linear survival models, 2) formalize an empirical process to evaluate the suitability of the required ancillary matching dataset, and 3) test modifications to the method to incorporate time varying exposure data, and proportional weighting of datasets.Methods: We used the association between fine particle air pollution (PM2.5) with mortality in the 2001 Canadian Census Health and Environment Cohort (CanCHEC, N = 2.4 million, 10-years follow-up) as our primary dataset, and the 2001 cycle of the Canadian Community Health Survey (CCHS, N = 80,630) as the ancillary matching dataset that contained confounding risk factor information not available in CanCHEC (e.g., smoking). The main evaluation process used a gold-standard approach wherein two variables (education and income) available in both datasets were excluded, indirectly adjusted for, and compared to true models with education and income included to assess the amount of bias correction. An internal validation for objective 1 used only CanCHEC data, whereas an external validation for objective 2 replaced CanCHEC with the CCHS. The two proposed modifications were applied as part of the validation tests, as well as in a final indirect adjustment of four missing risk factor variables (smoking, alcohol use, diet, and exercise) in which adjustment direction and magnitude was compared to models using an equivalent longitudinal cohort with direct adjustment for the same variables.Results: At baseline (2001) both cohorts had very similar PM2.5 distributions across population characteristics, although levels for CCHS participants were consistently 1.8-2.0 mu g/m(3) lower. Applying sample-weighting largely corrected for this discrepancy. The internal validation tests showed minimal downward bias in PM2.5 mortality hazard ratios of 0.4-0.6% using a static exposure, and 1.7-3% when a time-varying exposure was used. The external validation of the CCHS as the ancillary dataset showed slight upward bias of -0.7 to -1.1% and downward bias of 1.3-2.3% using the static and time-varying approaches respectively.Conclusions: The CCHS was found to be fairly well representative of CanCHEC and its use in Canada for indirect adjustment is warranted. Indirect adjustment methods can be used with survival models to correct hazard ratio point estimates and standard errors in models missing key covariates when a representative matching dataset is available. The results of this formal evaluation should encourage other cohorts to assess the suitability of ancillary datasets for the application of the indirect adjustment methodology to address potential residual confounding.
机译：背景技术：通过分区回归间接调整是控制大型流行病学研究中未测量混淆的有希望的技术。该方法使用代表性的辅助数据集来估计主要数据集中丢失的变量之间的关联，其具有辅助数据集的完整变量集，以产生所讨论的变量的调整风险估计。本文的目的是三倍：1）评估非线性生存模型的方法，2）正式化实证过程，评价所需的辅助匹配数据集的适用性，以及3）测试改进方法，以结合时间变化曝光的方法数据集的数据和比例加权。方法：我们在2001年加拿大人口普查健康和环境队列（CANCHEC，N = 240万，10年后续后）之间的死亡率之间的关联。作为我们的主要数据集，以及加拿大社区卫生调查（CCHS，N = 80,630）的2001年周期作为辅助匹配数据集，其包含CANCHEC（例如，吸烟）不可用的混淆风险因素信息。主要评估过程使用了一种金标准方法，其中两个数据集中可用的两个变量（教育和收入）被排除在一起，间接调整，并与具有教育和收入的真实模型进行了评估，以评估偏置校正量。目标1仅使用CANCHEC数据的内部验证，而客观2的外部验证将使用CCHS替换CANCHEC。作为验证测试​​的一部分应用了两个提出的修改，以及在使用调整方向和幅度的最终间接调整的四个缺失的风险因子变量（吸烟，酒精使用，饮食和锻炼）中，与使用相同的纵向队列，直接调整相同的变量。结果：在基线（2001），两个队列在人口特征上具有非常相似的PM2.5分布，尽管CCHS参与者的水平始终为1.8-2.0μg/ m（3）。应用样品加权在很大程度上校正这种差异。内部验证测试在使用静电暴露时显示出0.4-0.6％的PM2.5死亡率危险比的最小下降偏差，并且当使用时变暴露时，1.7-3％。 CCHS作为辅助数据集的外部验证显示，使用静态和时变方法分别显示出-0.7至-1.1％和下降1.3-2.3％的偏差。结论：CCHS被认为是相当好的代表性CONCHEC及其在加拿大的使用进行间接调整。间接调整方法可以与生存模型一起使用，以纠正危险比点估计和标准错误在缺少钥匙协变的模型中，当代表匹配数据集可用时缺少密钥协变量。此正式评估的结果应鼓励其他群组评估辅助数据集适用于应用间接调整方法，以解决潜在的剩余混杂性。

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