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

TL;DR

This paper introduces the joint variable importance plot, a new visualization tool that helps prioritize variables in observational studies by combining their associations with treatment and outcome, improving confounder adjustment.

Contribution

The paper proposes the joint variable importance plot for better variable prioritization, incorporating outcome associations and bias curves, aiding in observational study design.

Findings

Effective variable prioritization for matching and weighting methods.

Application to study of glyburide and C-section delivery.

Enhanced confounder adjustment through the proposed visualization.

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 bias curves. Variable prioritization using the plot can produce recommended values for tuning parameters in many existing matching and weighting methods. We showcase the use of the joint variable importance plots in the design of a balance-constrained matched study to evaluate whether taking an antidiabetic medication, glyburide, increases the incidence of C-section delivery among pregnant individuals with gestational diabetes.