INCREMENTAL EXPENDITURE OF BIOLOGIC DISEASE MODIFYING ANTIRHEUMATIC TREATMENT USING INSTRUMENTAL VARIABLES IN PANEL DATA
提出一种基于广义矩估计的面板数据工具变量方法，用于同时估计多种生物类抗风湿药物的治疗效果，解决真实世界数据中的选择偏倚和异质性问题，对比较效果研究和卫生技术评估有广泛适用性。
In health care, decision makers are generally interested in simultaneous comparisons among multiple treatments or interventions available as treatment choices in real-world clinical setting. The lack of random assignment to treatment in real-world clinical settings leads to selection-bias issues when evaluating the marginal benefits of treatment. The application of instrumental variables (IV) estimation to mitigate selection bias has traditionally been limited to comparing only two treatments/interventions concurrently. Using the case of biologic treatment in rheumatoid arthritis, we describe a generalized method of moments (GMM)-based panel data IV (IV-GMM) framework, to simultaneously estimate multiple treatment effects in the presence of time-varying selection bias and time-invariant heterogeneity. To satisfy the order and rank conditions for identification with multiple endogeneity, we propose lagged values of each treatment as excluded instruments. We evaluate the validity of the IV estimation assumptions on instrument relevance and exogeneity. Results indicate that the IV-GMM model offers enhanced control over selection bias and heterogeneity, and more importantly the panel data framework can provide valid excluded instruments that satisfy the order and rank conditions for identification when dealing with multiple endogenous variables. The approach outlined in this article has broad application for comparative effectiveness and health technology assessment involving multiple treatments/interventions using real-world nonexperimental data.