Applications and realizations of change-in-estimate and directed acyclic graph in linear regression variable screen
Sheng Song
Guo Manping
Zhao Yang
Huang Ye
Abstract：Objective To display how to use online tools-DAGitty and R to realize directed acyclic graph(DAG)combined with change-in-estimate(CIE)for screening confounders need to be adjusted in linear regression.Methods It was assumed that exposure factor was BMI_ICU,outcome was Death_28D and there were 9 confounder factors including Sex based on data packages of Jung and Su-Young J et al.(2019)from DATADRYAD.Taken a logistic regression model as an example,firstly an online tool,DAGitty,was applied to visualize the cause and effect relationships between variables,and to automatically identify the confounders needed to be adjusted.R was then used for batch screening confounders which changed estimate size of exposure factor over 10％.Results There was no mediating variable found by DAG.when confounders were sequentially added to the univariate model or removed from the full-variable model,the effect size of BMI_ICU changed by less than 10％.In summary,there were no confounders that required to be adjusted.Conclusion The screen of confounders in linear regression can be easily,effectively and quickly conducted by DAG combined with CIE through DAGitty and R.The code and method of other linear regression models need be slightly modified on this basis.
Keywords：Directed acyclic graphChange-in-estimateDAGittyConfounder screeningLinear regression
Publication Date：2024-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 1175-1178 )