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引用次数: 0
摘要
在工具变量(IV)环境中,如不完全随机试验和孟德尔随机化的观察研究中,我们可能会遇到一个连续的暴露因子,但其因果效应并不是我们真正感兴趣的。相反,科学兴趣可能在于这种暴露的粗略版本。尽管有大量文献研究了粗略化暴露的影响,其中有几部著作特别关注 IV 设置,但这些文献中提出的所有方法都需要参数假设。相反,就像在标准 IV 设置中一样,我们可以通过不带参数假设的约束来考虑部分识别。Alexander Balke 的博士论文首次指出了这一点。我们对他的工作进行了扩展和澄清,并在几种情况下推导出了新的边界,包括三层 IV,这很可能是孟德尔随机化的情况。我们在两个真实数据示例中展示了我们的发现,一个是针对婴儿花生过敏的随机试验,另一个是调查同型半胱氨酸对心血管疾病影响的孟德尔随机设置。
The impact of coarsening an exposure on partial identifiability in instrumental variable settings.
In instrumental variable (IV) settings, such as imperfect randomized trials and observational studies with Mendelian randomization, one may encounter a continuous exposure, the causal effect of which is not of true interest. Instead, scientific interest may lie in a coarsened version of this exposure. Although there is a lengthy literature on the impact of coarsening of an exposure with several works focusing specifically on IV settings, all methods proposed in this literature require parametric assumptions. Instead, just as in the standard IV setting, one can consider partial identification via bounds making no parametric assumptions. This was first pointed out in Alexander Balke's PhD dissertation. We extend and clarify his work and derive novel bounds in several settings, including for a three-level IV, which will most likely be the case in Mendelian randomization. We demonstrate our findings in two real data examples, a randomized trial for peanut allergy in infants and a Mendelian randomization setting investigating the effect of homocysteine on cardiovascular disease.
期刊介绍:
Among the important scientific developments of the 20th century is the explosive growth in statistical reasoning and methods for application to studies of human health. Examples include developments in likelihood methods for inference, epidemiologic statistics, clinical trials, survival analysis, and statistical genetics. Substantive problems in public health and biomedical research have fueled the development of statistical methods, which in turn have improved our ability to draw valid inferences from data. The objective of Biostatistics is to advance statistical science and its application to problems of human health and disease, with the ultimate goal of advancing the public''s health.