Gomez David Benjamin, Xu Zhaoyi, Saleh Joseph Homer
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Patterns (N Y). 2020 Nov 27;1(9):100154. doi: 10.1016/j.patter.2020.100154. eCollection 2020 Dec 11.
In the absence of direct measurements of state-level household gun ownership (GO), the quality and accuracy of proxy measures for this variable are essential for firearm-related research and policy development. In this work, we develop two highly accurate proxy measures of GO using traditional regression analysis and deep learning, the former accounting for non-linearities in the covariates (portion of suicides committed with a firearm [FS/S] and hunting license rates) and their statistical interactions. We subject the proxies to extensive model diagnostics and validation. Both our regression-based and deep-learning proxy measures provide highly accurate models of GO with training R of 96% and 98%, respectively, along with other desirable qualities-stark improvements over the prevalent FS/S proxy (R = 0.68). Model diagnostics reveal this widely used FS/S proxy is highly biased and inadequate; we recommend that it no longer be used to represent state-level household gun ownership in firearm-related studies.
在缺乏对州级家庭枪支拥有情况（GO）进行直接测量的情况下，该变量代理指标的质量和准确性对于枪支相关研究及政策制定至关重要。在本研究中，我们运用传统回归分析和深度学习开发了两种高度准确的GO代理指标，前者考虑了协变量（使用枪支自杀的比例[FS/S]和狩猎许可证率）中的非线性及其统计交互作用。我们对这些代理指标进行了广泛的模型诊断和验证。我们基于回归和深度学习的代理指标均提供了高度准确的GO模型，训练R分别为96%和98%，以及其他理想特性——相较于普遍使用的FS/S代理指标（R = 0.68）有显著改进。模型诊断显示，这种广泛使用的FS/S代理指标存在高度偏差且不充分；我们建议在枪支相关研究中不再使用它来代表州级家庭枪支拥有情况。
