  Estimates of the effects of treatment on cost from observational studies are
subject to bias if there are unmeasured confounders. It is therefore advisable
in practice to assess the potential magnitude of such biases. We derive a
general adjustment formula for loglinear models of mean cost and explore
special cases under plausible assumptions about the distribution of the
unmeasured confounder. We assess the performance of the adjustment by
simulation, in particular, examining robustness to a key assumption of
conditional independence between the unmeasured and measured covariates given
the treatment indicator. We apply our method to SEER-Medicare cost data for a
stage II/III muscle-invasive bladder cancer cohort. We evaluate the costs for
radical cystectomy vs. combined radiation/chemotherapy, and find that the
significance of the treatment effect is sensitive to plausible unmeasured
Bernoulli, Poisson and Gamma confounders.
