  Nonparametric estimators of the mean total cost have been proposed in a
variety of settings. In clinical trials it is generally impractical to follow
up patients until all have responded, and therefore censoring of patient
outcomes and total cost will occur in practice. We describe a general
longitudinal framework in which costs emanate from two streams, during sojourn
in health states and in transition from one health state to another. We
consider estimation of net present value for expenditures incurred over a
finite time horizon from medical cost data that might be incompletely
ascertained in some patients. Because patient specific demographic and clinical
characteristics would influence total cost, we use a regression model to
incorporate covariates. We discuss similarities and differences between our net
present value estimator and other widely used estimators of total medical
costs. Our model can accommodate heteroscedasticity, skewness and censoring in
cost data and provides a flexible approach to analyses of health care cost.
