Biostatistics and Biomathematics Unit, Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Ramat Gan 52621, Israel.
Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Ramat Gan 52621, Israel; Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
J Clin Epidemiol. 2023 Sep;161:46-52. doi: 10.1016/j.jclinepi.2023.07.001. Epub 2023 Jul 10.
To examine the effects of reverse causation on estimates from the weighted cumulative exposure (WCE) model that is used in pharmacoepidemiology to explore drug-health outcome associations, and to identify sensitivity analyses for revealing such effects.
314,099 patients with diabetes under Clalit Health Services, Israel, were followed over 2002-2012. The association between metformin and pancreatic cancer (PC) was explored using a WCE model within the framework of discrete-time Cox regression. We used computer simulations to explore the effects of reverse causation on estimates of a WCE model and to examine sensitivity analyses for revealing and adjusting for reverse causation. We then applied those sensitivity analyses to our data.
Simulation demonstrated bias in the weighted cumulative exposure model and showed that sensitivity analysis could reveal and adjust for these biases. In our data, a positive association was observed (hazard ratio (HR) = 3.24, 95% confidence interval (CI): 2.24-4.73) with metformin exposure in the previous 2 years. After applying sensitivity analysis, assuming reverse causation operated up to 4 years before cancer diagnosis, the association between metformin and PC was no longer apparent.
Reverse causation can cause substantial bias in the WCE model. When suspected, sensitivity analyses based on causal analysis are advocated.
探讨在药物流行病学中用于探索药物-健康结果相关性的加权累计暴露（WCE）模型中，反向因果关系对估计值的影响，并确定用于揭示此类影响的敏感性分析。
对以色列克拉利特健康服务机构的 314099 名糖尿病患者进行了 2002-2012 年的随访。在离散时间 Cox 回归框架内，使用 WCE 模型探讨二甲双胍与胰腺癌（PC）之间的关联。我们使用计算机模拟来探讨反向因果关系对 WCE 模型估计值的影响，并检查用于揭示和调整反向因果关系的敏感性分析。然后，我们将这些敏感性分析应用于我们的数据。
模拟结果表明，加权累计暴露模型存在偏差，并表明敏感性分析可以揭示和调整这些偏差。在我们的数据中，观察到二甲双胍在前 2 年的暴露与胰腺癌之间存在正相关（风险比（HR）= 3.24，95%置信区间（CI）：2.24-4.73）。在应用敏感性分析后，假设反向因果关系在癌症诊断前 4 年内起作用，二甲双胍与 PC 之间的关联不再明显。
反向因果关系会导致 WCE 模型产生重大偏差。当怀疑存在反向因果关系时，提倡基于因果分析的敏感性分析。
