In environmental health research, it is of interest to understand the effect of the neighborhood environment on health. Researchers have shown a protective association between green space around a person's residential address and depression outcomes. In measuring exposure to green space, distance buffers are often used. However, buffer distances differ across studies. Typically, the buffer distance is determined by researchers a priori. It is unclear how to identify an appropriate buffer distance for exposure assessment. To address geographic uncertainty problem for exposure assessment, we present a domain selection algorithm based on the penalized functional linear Cox regression model. The theoretical properties of our proposed method are studied and simulation studies are conducted to evaluate finite sample performances of our method. The proposed method is illustrated in a study of associations of green space exposure with depression and/or antidepressant use in the Nurses' Health Study.

翻译：在环境健康研究中，理解邻里环境对健康的影响具有重要意义。已有研究表明，住宅周边绿色空间与抑郁症结局之间存在保护性关联。在测量绿色空间暴露时，常采用距离缓冲区方法，但不同研究中的缓冲区距离存在差异。通常，缓冲区距离由研究者预先确定，目前尚不清楚如何确定合适的暴露评估缓冲区距离。针对暴露评估的地理不确定性难题，本文提出一种基于惩罚函数线性Cox回归模型的域选择算法。我们研究了所提方法的理论性质，并通过模拟实验评估了其有限样本性能。该方法在护士健康研究中关于绿色空间暴露与抑郁症及/或抗抑郁药物使用关联的研究中得到了应用验证。