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The only pharmacologic treatment for gestational diabetes (GDM) approved byU.S. Food and Drug Administration is insulin. However, due to improved ease ofuse and lower cost, oral antidiabetic medications, such as glyburide, areprescribed more commonly than insulin. We investigate glyburide's impact on twoadverse perinatal outcomes compared to medical nutritional therapy, theuniversal first-line therapy, in a large, population-based cohort. At thedesign stage, we employ matching to select comparable treated subjects(receivedglyburide) and controls (received medical nutritional therapy). Multiplebackground variables were associated with GDM treatment modality and perinataloutcomes; however, there is ambiguity about which of the many potentialconfounding variables should be prioritized in matching. Standard selectionmethods based on treatment imbalance alone neglect variables' relationshipswith the outcome. Thus, we propose the joint variable importance plot(jointVIP) to guide variable prioritization for this study. This plot addsoutcome associations on a second dimension to better contextualize standardimbalance measures, further enhances variable comparisons using unadjusted biascurves derived under the omitted variable bias framework, and can producerecommended values for tuning parameters in existing methods. After formingmatched pairs, we conduct inference for adverse effects of glyburide andperform sensitivity analyses to assess the potential role of unmeasuredconfounding. Our findings of no reliable adverse effect of glyburide informfuture pharmacologic treatment strategies to manage GDM.

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