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Prediction equations of forced oscillation technique: the insidious role of collinearity

机译:强迫振荡技术的预测方程:共线性的隐性作用

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摘要

Many studies have reported reference data for forced oscillation technique (FOT) in healthy children. The prediction equation of FOT parameters were derived from a multivariable regression model examining the effect of age, gender, weight and height on each parameter. As many of these variables are likely to be correlated, collinearity might have affected the accuracy of the model, potentially resulting in misleading, erroneous or difficult to interpret conclusions.The aim of this work was: To review all FOT publications in children since 2005 to analyze whether collinearity was considered in the construction of the published prediction equations. Then to compare these prediction equations with our own study. And to analyse, in our study, how collinearity between the explanatory variables might affect the predicted equations if it was not considered in the model. The results showed that none of the ten reviewed studies had stated whether collinearity was checked for. Half of the reports had also included in their equations variables which are physiologically correlated, such as age, weight and height. The predicted resistance varied by up to 28% amongst these studies. And in our study, multicollinearity was identified between the explanatory variables initially considered for the regression model (age, weight and height). Ignoring it would have resulted in inaccuracies in the coefficients of the equation, their signs (positive or negative), their 95% confidence intervals, their significance level and the model goodness of fit. In Conclusion with inaccurately constructed and improperly reported models, understanding the results and reproducing the models for future research might be compromised.
机译:许多研究报告了健康儿童的强迫振荡技术(FOT)的参考数据。 FOT参数的预测方程式是从多变量回归模型得出的,该模型检查了年龄,性别,体重和身高对每个参数的影响。由于其中许多变量可能相关,共线性可能影响模型的准确性,从而可能导致误导,错误或难以解释的结论。这项工作的目的是:回顾2005年以来所有儿童FOT出版物。分析在发布的预测方程的构造中是否考虑共线性。然后将这些预测方程与我们自己的研究进行比较。并且为了分析,在我们的研究中,如果模型中未考虑解释变量之间的共线性如何影响预测方程。结果表明,十项综述研究均未说明是否检查了共线性。一半的报告还在方程式中包括了与生理相关的变量,例如年龄,体重和身高。在这些研究中,预测的耐药性变化高达28%。并且在我们的研究中,在最初考虑用于回归模型的解释变量(年龄,体重和身高)之间确定了多重共线性。忽略它会导致方程系数,正负号(正负),95%置信区间,显着性水平和模型拟合优度不准确。结论在构建模型不正确且报告模型不正确的情况下,可能会影响理解结果并复制模型以供将来研究使用。

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