To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders and instrumental variables (IVs). Because such approaches are largely characterized by untestable assumptions, analysts must operate under an indefinite paradigm that these methods will work imperfectly. In this tutorial, we formalize a set of general principles and heuristics for estimating causal effects in the two approaches when the assumptions are potentially violated. This crucially requires reframing the process of observational studies as hypothesizing potential scenarios where the estimates from one approach are less inconsistent than the other. While most of our discussion of methodology centers around the linear setting, we touch upon complexities in non-linear settings and flexible procedures such as target minimum loss-based estimation (TMLE) and double machine learning (DML). To demonstrate the application of our principles, we investigate the use of donepezil off-label for mild cognitive impairment (MCI). We compare and contrast results from confounder and IV methods, traditional and flexible, within our analysis and to a similar observational study and clinical trial.
翻译:为了估计因果效应,卫生领域的观察研究分析师利用几种策略以减少由干扰变量产生的偏差。为此,分为两大类方法:使用干扰变量和工具变量(IV)方法。由于这些方法主要以无法测试的假设为特征,分析师必须在不确定这些方法会做不完美的前提下进行。在本教程中,我们为估计这两个方法中存在潜在假设违背情况下的因果效应形式化一组通用原则和启发式方法。这需要将观察性研究的过程重新定位,作为假设潜在情境,其中一个方法的估计值比另一个方法的估计值更加不一致。虽然我们大部分的方法论讨论聚焦于线性设置,但我们也挑选了非线性设置和灵活程序(如目标最小损失估计法和双机器学习)进行讨论。为演示我们原则的应用,我们研究了多奈哌齐作为治疗轻度认知障碍的偏方用法。我们比较和对比了我们分析中基于干扰变量和干扰因素的传统和灵活方法,并与类似观察研究和临床试验的结果进行比较。