论文标题
间隔因子无效假设的贝叶斯门德利随机测试：三元决策规则和损失功能校准
Bayesian Mendelian randomization testing of interval causal null hypotheses: ternary decision rules and loss function calibration
论文作者
论文摘要
我们采用孟德尔随机分析（MR）分析的方法旨在提高因果效应“发现”的可重复性：（i）使用贝叶斯的推理方法； （ii）用实用等价的区域代替点零假设，该区域由感兴趣的效果的值可忽略不计的值，同时利用贝叶斯分析能力量化了该区域内部/外部效果的证据； （iii）拒绝通常的二进制决策逻辑，而有利于三元逻辑，其中假设检验可能会导致接受或拒绝零，同时还可以满足“不确定”的结果。我们提出了通过损耗函数对所提出的方法进行校准的方法，我们用它将我们的方法与常见主义者进行比较。我们借助研究了肥胖对少年心肌梗塞风险的因果关系的研究来说明方法。
Our approach to Mendelian Randomization (MR) analysis is designed to increase reproducibility of causal effect "discoveries" by: (i) using a Bayesian approach to inference; (ii) replacing the point null hypothesis with a region of practical equivalence consisting of values of negligible magnitude for the effect of interest, while exploiting the ability of Bayesian analysis to quantify the evidence of the effect falling inside/outside the region; (iii) rejecting the usual binary decision logic in favour of a ternary logic where the hypothesis test may result in either an acceptance or a rejection of the null, while also accommodating an "uncertain" outcome. We present an approach to calibration of the proposed method via loss function, which we use to compare our approach with a frequentist one. We illustrate the method with the aid of a study of the causal effect of obesity on risk of juvenile myocardial infarction.
