Using Joint Variable Importance Plots to Prioritize Variables in Assessing the Impact of Glyburide on Adverse Birth Outcomes

The only pharmacologic treatment for gestational diabetes (GDM) approved by U.S. Food and Drug Administration is insulin. However, due to improved ease of use and lower cost, oral antidiabetic medications, such as glyburide, have been prescribed more commonly than insulin. We investigate glyburide's impact on two adverse perinatal outcomes compared to medical nutritional therapy, the universal first-line therapy, in an observational study of a large, population-based cohort. At the design stage, we employ matching to select comparable treated (received glyburide) and controls (received medical nutritional therapy). Multiple background variables were associated with GDM treatment modality and perinatal outcomes; however, there is ambiguity about which potential variables should be selected for matching. Standard selection methods based on treatment imbalance neglect the variable's relationship with the outcome. Thus, we propose the joint variable importance plot (jointVIP) to guide variable prioritization for this study. These plots enhance variable comparisons via bias curves derived under the omitted variable bias framework and can translate into recommended values for tuning parameters in existing methods. After forming matched pairs, we conduct inference and perform sensitivity analyses to assess the potential role of unmeasured confounding. Understanding glyburide safety can better inform future pharmacologic treatment strategies to manage GDM.