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.