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.

翻译：我们的门德利随机化(MR)分析方法旨在通过以下方式提高因果关系“发现”的可复制性:(一) 采用巴耶斯式的推理方法;(二) 以一个实际等值区域取代无效假设点,这个区域由利息效应的微小数值组成,同时利用巴耶斯式的分析能力量化区域内/外影响的证据;(三) 拒绝通常的二进制决定逻辑,赞成一种长期逻辑,即假设测试可能导致接受或拒绝无效,同时顾及“不确定”的结果;我们提出一种通过损失函数校准拟议方法的方法的方法,我们用这个方法来比较我们的方法与经常现象的方法;我们用研究肥胖对青少年心肌梗塞风险的因果关系的方法来说明这一方法。