Matsouaka Roland A, Tchetgen Tchetgen Eric J
Department of Biostatistics and Bioinformatics & Duke Clinical Research Institute Duke University, Durham, NC 27705, USA.
Departments of Biostatistics & Epidemiology, Harvard University, Boston, MA 02115, USA.
Biostatistics. 2017 Jul 1;18(3):465-476. doi: 10.1093/biostatistics/kxw059.
We consider estimating causal odds ratios using an instrumental variable under a logistic structural nested mean model (LSNMM). Current methods for LSNMMs either rely heavily on possible "uncongenial" modeling assumptions or involve intricate numerical challenges, which have impeded their use. In this article, we present an alternative method that ensures a congenial parametrization, circumvents computational complexity of existing methods, and is easy to implement. We illustrate the proposed method to (1) estimate the causal effect of years of education on earnings using data from the NLSYM and (2) assess the impact of moving families from high to low-poverty neighborhoods had on lifetime major depressive disorder among adolescents in the "Moving to Opportunity (MTO) for Fair Housing Demonstration Project" from the Department of Housing and Urban Development.
我们考虑在逻辑结构嵌套均值模型（LSNMM）下使用工具变量估计因果优势比。当前用于LSNMM的方法要么严重依赖可能“不一致”的建模假设，要么涉及复杂的数值挑战，这阻碍了它们的使用。在本文中，我们提出了一种替代方法，该方法确保了一致的参数化，规避了现有方法的计算复杂性，并且易于实施。我们举例说明了所提出的方法：（1）使用来自NLSYM的数据估计受教育年限对收入的因果效应；（2）评估从高贫困社区搬到低贫困社区对美国住房和城市发展部“公平住房示范项目中的机会迁移（MTO）”中青少年终生重度抑郁症的影响。
