Yang Ce, Zhang Ning, Li Jiaxuan, Mehta Unnati V, Hart Jaime E, Spiegelman Donna L, Wang Molin
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA.
Stat Med. 2025 Jul;44(15-17):e70191. doi: 10.1002/sim.70191.
Environmental epidemiologists are often interested in estimating the effect of time-varying functions of the exposure history on health outcomes. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually subject to measurement errors. To obtain unbiased estimates of the effects of such mismeasured functions in longitudinal studies with discrete outcomes, a method applicable to the main study/validation study design is developed. Various estimation procedures are explored. Simulation studies were conducted to assess its performance compared to standard analysis, and we found that the proposed method had good performance in terms of finite sample bias reduction and nominal coverage probability improvement. As an illustrative example, we applied the new method to a study of long-term exposure to , in relation to the occurrence of anxiety disorders in the Nurses' Health Study II. Failing to correct the error-prone exposure can lead to an underestimation of the chronic exposure effect of .
环境流行病学家常常对估计暴露史的时变函数对健康结果的影响感兴趣。然而，构成用于构建暴露史函数的暴露史的个体暴露测量值通常存在测量误差。为了在具有离散结局的纵向研究中获得对此类测量有误的函数的效应的无偏估计，开发了一种适用于主研究/验证研究设计的方法。探索了各种估计程序。进行了模拟研究以评估其与标准分析相比的性能，我们发现所提出的方法在减少有限样本偏差和提高名义覆盖概率方面具有良好性能。作为一个说明性示例，我们将新方法应用于护士健康研究II中关于长期暴露于 与焦虑症发生关系的研究。未能校正容易出错的暴露可能导致对 的慢性暴露效应的低估。
