Center for Opioid Epidemiology and Policy, Division of Epidemiology, Department of Population Health, New York University Grossman School of Medicine.
Department of Epidemiology, Boston University School of Public Health.
Psychol Trauma. 2023 Sep;15(6):917-929. doi: 10.1037/tra0001370. Epub 2022 Oct 13.
Researchers are often interested in assessing the causal effect of an exposure on an outcome when randomization is not ethical or feasible. Estimating causal effects by controlling for confounders can be unconvincing because important potential confounders remain unmeasured. Study designs leveraging instrumental variables (IVs) offer alternatives to confounder-control methods but are rarely used in stress and trauma research.
We review the conceptual foundations and implementation of IV methods. We discuss strengths and limitations of IV approaches, contrasting with confounder-control methods, and illustrate the relevance of IVs for stress and trauma research.
IV approaches leverage an external or exogenous source of variation in the exposure. Instruments are variables that meet three conditions: relevance (variation in the IV is associated with variation in the chance of exposure), exclusion (the IV only affects the outcome through the exposure), and exchangeability (no unmeasured confounding of the IV-outcome relationship). Interpreting estimates from IV analyses requires an additional assumption, such as monotonicity (the instrument does not change the chance of exposure in different directions for any two individuals). Valid IVs circumvent the need to correctly identify, measure, and control for all confounders of the exposure-outcome relationship. The primary challenge is identifying a valid instrument.
IV approaches have strengths and weaknesses compared with confounder-control approaches. IVs offers a promising complementary study design to improve evidence about the causal effects of exposures on outcomes relevant to stress and trauma. Collaboration with scientists who are experienced with identifying and analyzing IVs will support this work. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
当随机化不道德或不可行时,研究人员通常有兴趣评估暴露对结果的因果效应。通过控制混杂因素来估计因果效应可能没有说服力,因为重要的潜在混杂因素仍然未被测量。利用工具变量 (IV) 的研究设计为混杂因素控制方法提供了替代方案,但在压力和创伤研究中很少使用。
我们回顾了 IV 方法的概念基础和实施。我们讨论了 IV 方法的优缺点,与混杂因素控制方法进行了对比,并说明了 IV 方法在压力和创伤研究中的相关性。
IV 方法利用暴露的外部或外源性变化源。工具变量是满足以下三个条件的变量:相关性(工具变量的变化与暴露机会的变化相关)、排除性(工具变量仅通过暴露影响结果)和可交换性(工具变量与结果的关系没有未测量的混杂)。从 IV 分析中解释估计值需要额外的假设,例如单调性(对于任何两个人,工具变量不会以不同的方向改变暴露的机会)。有效的 IV 可以避免正确识别、测量和控制暴露-结果关系的所有混杂因素的需要。主要的挑战是确定有效的工具变量。
与混杂因素控制方法相比,IV 方法具有优势和劣势。IV 为改善与压力和创伤相关的暴露对结果的因果效应的证据提供了一种有前途的补充研究设计。与经验丰富的识别和分析 IV 的科学家合作将支持这项工作。(PsycInfo 数据库记录(c)2023 APA,保留所有权利)。