In pharmacoepidemiology research, instrumental variables (IVs) are variables that strongly predict treatment but have no causal effect on the outcome of interest except through the treatment. There remain concerns about the inclusion of IVs in propensity score (PS) models amplifying estimation bias and reducing precision. Some PS modeling approaches attempt to address the potential effects of IVs, including selecting only covariates for the PS model that are strongly associated to the outcome of interest, thus screening out IVs. We conduct a study utilizing simulations and negative control experiments to evaluate the effect of IVs on PS model performance and to uncover best PS practices for real-world studies. We find that simulated IVs have a weak effect on bias and precision in both simulations and negative control experiments based on real-world data. In simulation experiments, PS methods that utilize outcome data, including the high-dimensional propensity score, produce the least estimation bias. However, in real-world settings underlying causal structures are unknown, and negative control experiments can illustrate a PS model's ability to minimize systematic bias. We find that large-scale, regularized regression based PS models in this case provide the most centered negative control distributions, suggesting superior performance in real-world scenarios.

翻译：在药物流行病学研究中，工具变量（IVs）是指能强预测治疗方案但仅通过治疗对结局产生因果影响的变量。目前仍存在对将工具变量纳入倾向性评分（PS）模型可能放大估计偏倚并降低精度的担忧。部分倾向性评分建模方法试图应对工具变量的潜在影响，包括仅选择与结局强相关的协变量纳入PS模型，从而筛除IVs。我们通过模拟研究和阴性对照实验评估IVs对PS模型性能的影响，并揭示真实世界研究的最佳PS实践。研究发现，基于真实世界数据的模拟实验和阴性对照实验中，模拟IVs对偏倚和精度的影响较弱。在模拟实验中，利用结局数据的PS方法（包括高维倾向性评分）产生的估计偏倚最小。然而在真实世界环境中，因果结构未知，阴性对照实验可展现PS模型最小化系统偏倚的能力。本研究发现，大规模正则化回归的PS模型在此类场景中能提供最居中的阴性对照分布，表明其在真实世界场景中具有更优性能。