贝叶斯工具变量方法在药品安全性主动监测中的应用:一项数据模拟研究

摘要

Objective To establish the model of Bayesian instrumental variable analysis in the active surveillance data of adverse drug reactions for controlling unmeasurable confounding and acquiring the accurate causal relation between the drug and adverse reaction. Me thods Hamilton Markov Chain Monte Carlo method was used to perform data simulation and parameter estimation. Further, the established model was compared with traditional models on bias and accuracy to assess the performance of different methods. Re s ults Bayesian instrumental variable analysis performed well and was the optimal method under the small sample, weak instrumental variable, strong unmeasurable confounding and rare treatments and outcomes. Conclus ion Bayesian instrumental variable analysis could improve bias and accuracy compared with traditional instrumental variable methods in active surveillance data of adverse drug reactions.%目的 建立适用于药品不良反应主动监测数据特点的贝叶斯工具变量分析模型,控制潜在、未知的混杂因素对药品-不良反应关联推断的影响,获得更准确的药品安全性信息.方法 采用汉密尔顿蒙特卡洛方法进行数据模拟和参数估计,对传统工具变量分析方法和贝叶斯工具变量方法在不同的参数设置情境下的结果进行比较和评价.结果 在小样本、弱工具变量、遗漏混杂因素强度强和处理因素与结局变量发生率低的情况下,贝叶斯工具变量分析得到估计量的绝对偏倚较小,置信区间宽度最窄,估计结果最稳定.结论 当主动监测研究中收集的数据量较小,关注的处理和结局因素为二分类变量且发生率较低时,贝叶斯工具变量分析可较好的控制潜在、未知混杂因素的影响,且与传统工具变量分析相比可提高处理效应估计的准确性和精确性.