Nested g-computation: a causal approach to analysis of censored medical costs in the presence of time-varying treatment

Rising medical costs are an emerging challenge in policy decisions and resource allocation planning. When cumulative medical cost is the outcome, right censoring induces informative missingness due to heterogeneity in cost accumulation rates across subjects. Inverse weighting approaches have been developed to address the challenge of informative cost trajectories in mean cost estimation, though these approaches generally ignore post-baseline treatment changes. In post-hysterectomy endometrial cancer patients, data from a linked database of Medicare records and the Surveillance, Epidemiology, and End Results programme of the National Cancer Institute reveal substantial within-subject variation in treatment over time. In such a setting, the utility of existing intent-to-treat approaches is generally limited. Estimates of the population mean cost under a hypothetical time-varying treatment regime can better assist with resource allocation when planning for a treatment policy change; such estimates must inherently take time-dependent treatment and confounding into account. We develop a nested g-computation approach to cost analysis to address this challenge, while accounting for censoring. We develop a procedure to evaluate sensitivity to departures from baseline treatment ignorability. We further conduct a variety of simulations and apply our nested g-computation procedure to 2-year costs from endometrial cancer patients.