Moodie Erica E M, Schulz Juliana
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Quebec, Canada.
Department of Decision Sciences, HEC Montréal, Quebec, Canada.
Stat Med. 2025 Jul;44(15-17):e70194. doi: 10.1002/sim.70194.
The positivity or experimental treatment assignment assumption is a fundamental requirement in causal analyses, invoked to ensure that identifiability holds without extrapolating beyond what the observed data can reveal. Positivity is well understood in the context of binary and categorical treatments, and has been thoroughly discussed-from how the assumption can be assessed to approaches that may be used when the assumption is suspected not to hold. Positivity extends to the context of continuous exposures, such as doses, however it has been given very little formal consideration. In this manuscript, we propose a method for assessing whether the positivity assumption is violated in a given dataset, relying on a principled concept in regression analysis. We demonstrate the diagnostic tool in various simulated settings, as well as in an application involving warfarin dosing.
阳性或实验性治疗分配假设是因果分析中的一项基本要求，用于确保在不超出观察数据所能揭示的范围进行外推的情况下保持可识别性。在二元和分类治疗的背景下，阳性假设已得到充分理解，并且已经进行了深入讨论——从如何评估该假设到当怀疑该假设不成立时可能使用的方法。阳性假设扩展到连续暴露的情况，如剂量，但对此几乎没有进行过正式的考虑。在本手稿中，我们提出了一种方法，用于评估给定数据集中阳性假设是否被违反，该方法依赖于回归分析中的一个原则性概念。我们在各种模拟场景以及一个涉及华法林剂量的应用中展示了这种诊断工具。
