Population Studies Division, Health Canada, Ottawa, Canada; Department of Mathematics and Statistics, Queen's University, Kingston, Canada.
Population Studies Division, Health Canada, Ottawa, Canada.
Environ Res. 2014 Oct;134:482-7. doi: 10.1016/j.envres.2014.05.016. Epub 2014 Jun 24.
Develop statistical methods for survival models to indirectly adjust hazard ratios of environmental exposures for missing risk factors.
A partitioned regression approach for linear models is applied to time to event survival analyses of cohort study data. Information on the correlation between observed and missing risk factors is obtained from ancillary data sources such as national health surveys. The relationship between the missing risk factors and survival is obtained from previously published studies. We first evaluated the methodology using simulations, by considering the Weibull survival distribution for a proportional hazards regression model with varied baseline functions, correlations between an adjusted variable and an adjustment variable as well as selected censoring rates. Then we illustrate the method in a large, representative Canadian cohort of the association between concentrations of ambient fine particulate matter and mortality from ischemic heart disease.
Indirect adjustment for cigarette smoking habits and obesity increased the fine particulate matter-ischemic heart disease association by 3%-123%, depending on the number of variables considered in the adjustment model due to the negative correlation between these two risk factors and ambient air pollution concentrations in Canada. The simulations suggested that the method yielded small relative bias (<40%) for most cohort designs encountered in environmental epidemiology.
This method can accommodate adjustment for multiple missing risk factors simultaneously while accounting for the associations between observed and missing risk factors and between missing risk factors and health endpoints.
为生存模型开发统计方法，以间接调整环境暴露因素的风险比，用于缺失的风险因素。
将线性模型的分区回归方法应用于队列研究数据的事件时间生存分析。观察到的和缺失的风险因素之间的相关性信息来自辅助数据源，如国家健康调查。缺失的风险因素与生存之间的关系来自先前发表的研究。我们首先通过考虑比例风险回归模型的威布尔生存分布、调整变量和调整变量之间的相关性以及选定的删失率，使用模拟来评估该方法。然后，我们在一个大型的、具有代表性的加拿大队列中说明了这种方法，该队列研究了环境细颗粒物浓度与缺血性心脏病死亡率之间的关系。
由于加拿大这两个风险因素与环境空气污染浓度之间存在负相关，因此对吸烟习惯和肥胖进行间接调整增加了细颗粒物与缺血性心脏病之间的关联，增加幅度为 3%-123%，具体取决于调整模型中考虑的变量数量。模拟表明，该方法对于环境流行病学中遇到的大多数队列设计，产生的相对偏差较小（<40%）。
该方法可以同时适应多个缺失风险因素的调整，同时考虑观察到的和缺失的风险因素之间的关联，以及缺失的风险因素和健康终点之间的关联。
