Börnhorst Claudia, Reinders Tammo, Rathmann Wolfgang, Bongaerts Brenda, Haug Ulrike, Didelez Vanessa, Kollhorst Bianca
Leibniz Institute for Prevention Research and Epidemiology - BIPS, Department of Biometry and Data Management, Bremen, Germany.
Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany.
Clin Epidemiol. 2021 Oct 28;13:1027-1038. doi: 10.2147/CLEP.S328342. eCollection 2021.
Investigating intended or unintended effects of sustained drug use is of high clinical relevance but remains methodologically challenging. This feasibility study aims to evaluate the usefulness of the parametric g-formula within a target trial for application to an extensive healthcare database in order to address various sources of time-related biases and time-dependent confounding.
Based on the German Pharmacoepidemiological Research Database (GePaRD), we estimated the pancreatic cancer incidence comparing two hypothetical treatment strategies for type 2 diabetes mellitus (T2DM), i.e., (A) sustained metformin monotherapy vs (B) combination therapy with DPP-4 inhibitors after one year metformin monotherapy. We included 77,330 persons with T2DM who started metformin therapy at baseline between 2005 and 2011. Key aspects for avoiding time-related biases and time-dependent confounding were the emulation of a target trial over a 7-year follow-up period and application of the parametric g-formula.
Over the 7-year follow-up period, 652 out of the 77,330 study subjects had a diagnosis of pancreatic cancer. Assuming no unobserved confounding, we found evidence that the metformin/DPP-4i combination therapy increased the risk of pancreatic cancer compared to a sustained metformin monotherapy (risk ratio: 1.47; 95% bootstrap CI: 1.07-1.94). The risk ratio decreased in sensitivity analyses addressing protopathic bias.
While protopathic bias could not fully be ruled out, and computational challenges necessitated compromises in the analysis, the g-formula and target trial emulation proved useful: Self-inflicted biases were avoided, observed time-varying confounding was adjusted for, and the estimated risks have a clear causal interpretation.
研究持续用药的预期或非预期效果具有高度临床相关性，但在方法上仍具有挑战性。本可行性研究旨在评估目标试验中的参数g公式在应用于广泛的医疗数据库时的有用性，以解决各种与时间相关的偏差和时间依赖性混杂因素。
基于德国药物流行病学研究数据库（GePaRD），我们比较了两种假设的2型糖尿病（T2DM）治疗策略下的胰腺癌发病率，即（A）持续二甲双胍单药治疗与（B）二甲双胍单药治疗一年后联合DPP-4抑制剂治疗。我们纳入了2005年至2011年基线时开始二甲双胍治疗的77330例T2DM患者。避免与时间相关的偏差和时间依赖性混杂因素的关键方面是在7年随访期内模拟目标试验以及应用参数g公式。
在7年随访期内，77330名研究对象中有652人被诊断为胰腺癌。假设不存在未观察到的混杂因素，我们发现有证据表明，与持续二甲双胍单药治疗相比，二甲双胍/DPP-4i联合治疗增加了胰腺癌风险（风险比：1.47；95%自抽样置信区间：1.07-1.94）。在针对原发病性偏差的敏感性分析中，风险比降低。
虽然不能完全排除原发病性偏差，并且计算挑战使得在分析中需要做出妥协，但g公式和目标试验模拟证明是有用的：避免了自致性偏差，对观察到的时间变化混杂因素进行了调整，并且估计的风险具有明确的因果解释。
