aDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA; bDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA; cHarvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA; and dDivision of Rheumatology, Allergy and Immunology, Section of Clinical Sciences, Brigham and Women's Hospital, Boston, MA.
Epidemiology. 2017 Nov;28(6):771-779. doi: 10.1097/EDE.0000000000000742.
The effect of an exposure on survival can be biased when the regression model is misspecified. Hazard difference is easier to use in risk assessment than hazard ratio and has a clearer interpretation in the assessment of effect modifications.
We proposed two doubly robust additive hazards models to estimate the causal hazard difference of a continuous exposure on survival. The first model is an inverse probability-weighted additive hazards regression. The second model is an extension of the doubly robust estimator for binary exposures by categorizing the continuous exposure. We compared these with the marginal structural model and outcome regression with correct and incorrect model specifications using simulations. We applied doubly robust additive hazard models to the estimation of hazard difference of long-term exposure to PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 microns) on survival using a large cohort of 13 million older adults residing in seven states of the Southeastern United States.
We showed that the proposed approaches are doubly robust. We found that each 1 μg m increase in annual PM2.5 exposure was associated with a causal hazard difference in mortality of 8.0 × 10 (95% confidence interval 7.4 × 10, 8.7 × 10), which was modified by age, medical history, socioeconomic status, and urbanicity. The overall hazard difference translates to approximately 5.5 (5.1, 6.0) thousand deaths per year in the study population.
The proposed approaches improve the robustness of the additive hazards model and produce a novel additive causal estimate of PM2.5 on survival and several additive effect modifications, including social inequality.
当回归模型指定不当时，暴露对生存的影响可能会产生偏差。风险差异比风险比更容易用于风险评估，并且在评估效应修饰时具有更清晰的解释。
我们提出了两种双重稳健加性危害模型来估计连续暴露对生存的因果危害差异。第一个模型是逆概率加权加性危害回归。第二个模型是通过对连续暴露进行分类，扩展了用于二进制暴露的双重稳健估计量。我们使用模拟比较了这些模型与边际结构模型和具有正确和错误模型指定的结果回归。我们应用双重稳健加性危害模型来估计 PM2.5（空气动力学直径小于或等于 2.5 微米的颗粒物）对居住在美国东南部七个州的 1300 万老年人的生存的长期暴露的危害差异。
我们表明所提出的方法是双重稳健的。我们发现，每年 PM2.5 暴露增加 1μg/m 与死亡率的因果危害差异相关，为 8.0×10（95%置信区间为 7.4×10，8.7×10），这与年龄、病史、社会经济地位和城市化有关。总体危害差异转化为研究人群中每年约 5.5（5.1，6.0）千例死亡。
所提出的方法提高了加性危害模型的稳健性，并产生了 PM2.5 对生存和几种加性效应修饰的新的加性因果估计，包括社会不平等。
