Two-part models for cost with zeros to decompose effects of covariates on probability of cost, mean nonzero cost, and mean total cost.

Health care cost data often contain many zero values, for patients who did not use any care. Two-part models with logistic models for part I, probability of use (ie, nonzero cost) and log-link models for part II, mean cost of use (ie, nonzero cost) are often used. Effects of exposures or covariates on total (marginal) cost are often of interest, and recent work has proposed useful methods. Factors that affect total cost do so through a combination of effects on probability of use and cost of use. Such a decomposition is needed to understand and act on factors that affect total cost, but little work has been done on this question. This paper presents two new methods for decomposing effects on total cost, namely, an adjusted approach based on log-binomial models for part I and a population average approach based on counterfactual arguments. Extensive simulations illustrate performance, interpretation, and robustness over a wide range of data features. The method is applied to risk-adjusted 30-day outpatient cardiac cost for patients following percutaneous coronary intervention from the Department of Veterans Affairs Clinical Assessment, Reporting, and Tracking Program. Results illustrate the simple decomposition of effects on total cost into components for probability of use and cost of use.