虚假关系
因果关系(物理学)
医学
混淆
重症监护医学
梅德林
计算机科学
病理
物理
量子力学
机器学习
政治学
法学
作者
José Dianti,Idunn S Morris,Martin Urner,Marcello Schmidt,George Tomlinson,Marcelo B. P. Amato,Lluís Blanch,Gordon D. Rubenfeld,Ewan C. Goligher
标识
DOI:10.1164/rccm.202206-1216ci
摘要
ICU clinicians rely on bedside physiological measurements to inform many routine clinical decisions. Because deranged physiology is usually associated with poor clinical outcomes, it is tempting to hypothesize that manipulating and intervening on physiological parameters might improve outcomes for patients. However, testing these hypotheses through mathematical models of the relationship between physiology and outcomes presents a number of important methodological challenges. These models reflect the theories of the researcher and can therefore be heavily influenced by one's assumptions and background beliefs. Model building must therefore be approached with great care and forethought, because failure to consider relevant sources of measurement error, confounding, coupling, and time dependency or failure to assess the direction of causality for associations of interest before modeling may give rise to spurious results. This paper outlines the main challenges in analyzing and interpreting these models and offers potential solutions to address these challenges.
科研通智能强力驱动
Strongly Powered by AbleSci AI