Abrahamowicz Michal, Bjerre Lise M, Beauchamp Marie-Eve, LeLorier Jacques, Burne Rebecca
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
Division of Clinical Epidemiology, McGill University Health Centre, Montreal, QC, Canada.
Stat Med. 2016 Mar 30;35(7):1001-16. doi: 10.1002/sim.6818. Epub 2016 Jan 14.
Unmeasured confounding is a major threat to the validity of pharmacoepidemiological studies of medication safety and effectiveness. We propose a new method for detecting and reducing the impact of unobserved confounding in large observational database studies. The method uses assumptions similar to the prescribing preference-based instrumental variable (IV) approach. Our method relies on the new 'missing cause' principle, according to which the impact of unmeasured confounding by (contra-)indication may be detected by assessing discrepancies between the following: (i) treatment actually received by individual patients and (ii) treatment that they would be expected to receive based on the observed data. Specifically, we use the treatment-by-discrepancy interaction to test for the presence of unmeasured confounding and correct the treatment effect estimate for the resulting bias. Under standard IV assumptions, we first proved that unmeasured confounding induces a spurious treatment-by-discrepancy interaction in risk difference models for binary outcomes and then simulated large pharmacoepidemiological studies with unmeasured confounding. In simulations, our estimates had four to six times smaller bias than conventional treatment effect estimates, adjusted only for measured confounders, and much smaller variance inflation than unbiased but very unstable IV estimates, resulting in uniformly lowest root mean square errors. The much lower variance of our estimates, relative to IV estimates, was also observed in an application comparing gastrointestinal safety of two classes of anti-inflammatory drugs. In conclusion, our missing cause-based method may complement other methods and enhance accuracy of analyses of large pharmacoepidemiological studies.
未测量的混杂因素是药物安全性和有效性的药物流行病学研究有效性的主要威胁。我们提出了一种新方法，用于在大型观察性数据库研究中检测和减少未观察到的混杂因素的影响。该方法使用与基于处方偏好的工具变量（IV）方法类似的假设。我们的方法依赖于新的“缺失原因”原则，根据该原则，可通过评估以下两者之间的差异来检测（反）适应症引起的未测量混杂因素的影响：（i）个体患者实际接受的治疗和（ii）基于观察数据预期他们会接受的治疗。具体而言，我们使用治疗与差异的交互作用来检验未测量混杂因素的存在，并对由此产生的偏差校正治疗效果估计值。在标准IV假设下，我们首先证明在二元结局的风险差异模型中，未测量的混杂因素会引起虚假的治疗与差异交互作用，然后模拟存在未测量混杂因素的大型药物流行病学研究。在模拟中，我们的估计值的偏差比仅针对测量到的混杂因素进行调整的传统治疗效果估计值小四到六倍，并且方差膨胀比无偏但非常不稳定的IV估计值小得多，从而导致均方根误差始终最低。在比较两类抗炎药胃肠道安全性的应用中，我们还观察到相对于IV估计值，我们估计值的方差要低得多。总之，我们基于缺失原因的方法可以补充其他方法，并提高大型药物流行病学研究分析的准确性。
