建立更好的模型:放弃厨房水槽回归

Stefan Kuhle, Mary Margaret Brown, Sanja Stanojevic
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引用次数: 0

摘要

本文对 "厨房水槽回归 "进行了批判性研究。"厨房水槽回归 "的特点是根据 p 值或基于模型的信息标准为多变量回归模型手动或自动选择变量。我们以围产期/新生儿医学为例,强调了这种方法的缺陷,并提出了更稳健的替代方法。介绍了有向无环图(DAG)的概念,作为描述和分析因果关系的工具。我们强调了 "厨房水槽回归 "的五个关键问题:(1) 忽视变量关系的方向性;(2) 对这些模型的效应估计缺乏有意义的因果解释;(3) 多重测试导致α误差率升高;(4) 存在过度拟合和模型不稳定的风险;(5) 在建立模型时忽视内容的专业性。我们主张使用 DAG 来指导旨在检验推定风险因素与结果之间关联的模型的变量选择,并强调在医学研究中需要更周到、更明智地使用回归模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building a better model: abandon kitchen sink regression
This paper critically examines ‘kitchen sink regression’, a practice characterised by the manual or automated selection of variables for a multivariable regression model based on p values or model-based information criteria. We highlight the pitfalls of this method, using examples from perinatal/neonatal medicine, and propose more robust alternatives. The concept of directed acyclic graphs (DAGs) is introduced as a tool for describing and analysing causal relationships. We highlight five key issues with ‘kitchen sink regression’: (1) the disregard for the directionality of variable relationships, (2) the lack of a meaningful causal interpretation of effect estimates from these models, (3) the inflated alpha error rate due to multiple testing, (4) the risk of overfitting and model instability and (5) the disregard for content expertise in model building. We advocate for the use of DAGs to guide variable selection for models that aim to examine associations between a putative risk factor and an outcome and emphasise the need for a more thoughtful and informed use of regression models in medical research.
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