孟德尔随机化(MR)分析使用基因型作为工具来估计在存在未观察到的混杂因素的情况下暴露的因果效应。现有的MR方法侧重于前瞻性队列研究产生的数据。我们开发了一种在病例对照设计下研究二元结果的程序。所提出的程序建立在通常用于MR分析的两个工作模型上,并采用准经验似然框架来解决病例对照采样的确定偏差。在经验似然框架下,我们推导了各种估计因果效应和假设检验的方法。我们进行了广泛的仿真研究,以评估提出的方法。我们发现,所提出的经验似然估计比现有估计有更小的偏差。在所有考虑的方法中,拉格朗日乘数(LM)测试具有最高的功率,从LM检验得出的置信区间具有最准确的覆盖率。我们说明了我们的方法在前列腺癌病例对照数据的MR分析中的使用,其中维生素D水平为暴露量,三个单核苷酸多态性为工具。
Mendelian randomization (MR) analysis uses genotypes as instruments to estimate the causal effect of an exposure in the presence of unobserved confounders. The existing MR methods focus on the data generated from prospective cohort studies. We develop a procedure for studying binary outcomes under a
case-control design. The proposed procedure is built upon two working models commonly used for MR analyses and adopts a quasi-empirical likelihood framework to address the ascertainment bias from
case-control sampling. We derive various approaches for estimating the causal effect and hypothesis testing under the empirical likelihood framework. We conduct extensive simulation studies to evaluate the proposed methods. We find that the proposed empirical likelihood estimate is less biased than the existing estimates. Among all the approaches considered, the Lagrange multiplier (LM) test has the highest power, and the confidence intervals derived from the LM test have the most accurate coverage. We illustrate the use of our method in MR analysis of prostate cancer
case-control data with vitamin D level as exposure and three single nucleotide polymorphisms as instruments.