Regression models for analysis of medical costs

Rising cost of health care and the need for evaluating costs of new medical interventions have led to interest in developing methods for medical cost analysis. Hospital costs constitute a significant proportion of overall expenditures in health care. Knowing the correlates of in-hospital length of stay (LOS) and cost is important for decisions on allocating resources.;Increasing availability of patient specific LOS and cost permits analysis of these variables jointly, accounting for their likely correlation. A bivariate model is used to assess the impact of covariates on these outcomes. Under marginal specification through parametric or Cox regression models for LOS and cost, standard errors of estimates of regression coefficients are obtained using a robust covariance matrix to account for correlation between LOS and cost that is otherwise left unspecified.;In another model, we use a conditional approach to estimate mean costs as a function of patient hospital stay and adjusts for the influence of patient characteristics on LOS and cost. The mean cost over a specified duration is a weighted average of the expected cumulative cost, with weighting determined by the distribution of LOS.;We extend this model to address costs and resource utilization in longitudinal studies when patient histories evolve through several health states. In these studies costs are incurred in random amounts at random times as patients transit through different health states. We describe the evolution of a patient's health history by a continuous time Markov process with finite state space. Dependence of the transition intensities on patient characteristics is modeled through semiparametric regression models. Two types of expenditures are incurred, one at transitions between health states and the other for sojourns in a health state. Over a fixed follow up period, we consider net present values of all costs incurred in this period for the two types of expenditures. Conditional on the initial state and a specified covariate vector, we obtain consistent estimates of the expected net present values and derive their asymptotic distributions.;Our methods provide flexible approaches to estimating medical costs while controlling for the effects of covariates. In addition, for economic evaluation studies of competing medical interventions, our methods can be applied to estimate summary statistics and cost-effectiveness ratios.