  There has recently been a growing interest in the development of statistical
methods to compare medical costs between treatment groups. When cumulative cost
is the outcome of interest, right-censoring poses the challenge of informative
missingness due to heterogeneity in the rates of cost accumulation across
subjects. Existing approaches seeking to address the challenge of informative
cost trajectories typically rely on inverse probability weighting and target a
net "intent-to-treat" effect. However, no approaches capable of handling
time-dependent treatment and confounding in this setting have been developed to
date. A method to estimate the joint causal effect of a treatment regime on
cost would be of value to inform public policy when comparing interventions. In
this paper, we develop a nested g-computation approach to cost analysis in
order to accommodate time-dependent treatment and repeated outcome measures. We
demonstrate that our procedure is reasonably robust to departures from its
distributional assumptions and can provide unique insights into fundamental
differences in average cost across time-dependent treatment regimes.
