The Nested G-formula: A Causal Approach for Analyzing Medical Cost Outcomes

Rising medical costs are of growing importance in guiding health policy decisions. Analyses of longitudinal studies with cost outcomes are often complicated by right-censoring, whereby complete costs are only available on a subset of participants. Existing methods seeking to address this challenge are intent-to-treat in nature, utilizing only baseline treatment status irrespective of any changes in treatment received. It is essential to take time-varying treatment and confounding into account to more adequately meet the goals of health policy guidance and resource allocation. In this talk, we formalize a nested g-computation procedure to target contrasts in marginal means under different hypothetical population-level treatment strategies. Simulations demonstrate that the nested g-computation procedure exhibits a fair amount of robustness to model misspecification. Based on an application to endometrial cancer using SEER-Medicare data, we further demonstrate that the nested g-formula is a flexible framework that can be used to gain insights into overall costs implied by competing policies.