Combines Wald ratio (or ratio estimates) together in fixed effects meta-analysis, where the weight of each ratio is the inverse of the variance of the association between the single nucleotide polymorphism (SNP) and the outcome. Here, each SNP being used as an instrumental variable (IV) is treated as an independent "study" (i.e., as in traditional meta-analyses), and the Wald ratios estimated for each SNP are meta-analysed under a fixed effects model.
在固定效应荟萃分析中，将沃尔德比率（或比率估计值）结合起来，其中每个比率的权重是单核苷酸多态性（SNP）与结局之间关联方差的倒数。在这里，每个用作工具变量（IV）的SNP都被视为一项独立的“研究”（即如同在传统荟萃分析中一样），并且在固定效应模型下对每个SNP估计的沃尔德比率进行荟萃分析。
The fixed effects meta-analysis assumes that the only source of differences between estimates across the studies is due to sampling variation (i.e., the true causal effect estimate is identical across all studies). In the MR context this translates to each SNP exhibiting no horizontal pleiotropy. To estimate a valid causal effect, genetic variants must be valid IVs. If all SNPs exhibit horizontal pleiotropy, then the effect estimate is asymptotically unbiased, but the standard error will be overly precise. This method uses weights that assume the association between the SNP and exposure is known, rather than estimated, with NO Measurement Error (i.e., known as the "NOME assumption"). Causal effect estimates from the IVW approach exhibit weak instrument bias whenever SNPs used as IVs violate the NOME assumption, which can be measured using the F-statistic with IVW methods. See NOME adjustment for more information on possible weights used in these models.
固定效应荟萃分析假设，研究间估计值差异的唯一来源是抽样变异（即所有研究的真实因果效应估计值相同）。在孟德尔随机化（MR）背景下，这意味着每个单核苷酸多态性（SNP）均不存在水平多效性。为了估计有效的因果效应，遗传变异必须是有效的工具变量（IVs）。如果所有单核苷酸多态性（SNPs）都存在水平多效性，那么效应估计值在渐近情况下是无偏的，但标准误会过于精确。该方法所使用的权重假设单核苷酸多态性（SNP）与暴露之间的关联是已知的，而非估计的，且不存在测量误差（即所谓的“无测量误差假设（NOME assumption）”）。当用作工具变量（IVs）的单核苷酸多态性（SNPs）违反无测量误差假设（NOME assumption）时，逆方差加权（IVW）法得到的因果效应估计值会出现弱工具变量偏倚，这可以通过逆方差加权（IVW）法中的F统计量来衡量。有关这些模型中可能使用的权重的更多信息，请参见无测量误差假设（NOME）调整。
