We develop an innovative method to assess total treatment costs over a finite period of time while incorporating the dynamics of change in the health status of patients. Costs are incurred through medical care use while patients sojourn in health states. Because complete ascertainment of costs and observation of events are not always feasible, some patient utilization will be incomplete and events will also be censored. A Markov model is used to estimate the transition probabilities between health states and the impact of patient variables on transition intensities. A mixed-effects model is used for sojourn costs with transition times as random effects and patient variables as fixed effects. The models are combined to estimate net present values (NPVs) of expenditures over a finite time interval as a function of patient characteristics. The method is applied to a data set of 624 incident cases of cancer. Physical functioning after cancer diagnosis was assessed periodically through structured interviews. The outcomes of interest are normal physical function, impaired physical function, or the terminal state, dead. Charges were obtained from Medicare claim files for 2 years following cancer diagnosis. For demonstration purposes, we estimate NPVs for charges incurred over 2 years by cancer site and cancer stage. Our method, a joint regression model, provides a flexible approach to assessing the influence of patient characteristics on both cost and health outcomes while accommodating heteroscedasticity, skewness and censoring in the data.