Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot.

ABSTRACT: Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline variables, and there is often ambiguity about which variables should be prioritized. Standard prioritization methods based on treatment imbalance alone neglect variables' relationships with the outcome. We propose the joint variable importance plot to guide variable prioritization for observational studies. Since not all variables are equally relevant to the outcome, the plot adds outcome associations to quantify the potential confounding jointly with the standardized mean difference. To enhance comparisons on the plot between variables with different confounding relationships, we also derive and plot

SUBMITTER: Liao LD 

PROVIDER: S-EPMC11460722 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature