Objective: Where patient level data are available on health care costs, it is natural to use statistical analysis to describe the differences in cost between alternative treatments. Health care costs are, however, commonly considered to be skewed, which could present problems for standard statistical tests. This review examines how authors report the distributional form of health care cost data and how they have analysed their results. Method: A review of cost-effectiveness studies that collected patient-level data on health care costs. To supplement the review, five datasets on health care costs are examined. Consideration is given to the use of parametric methods on the transformed scale and to non-parametric methods of analysing skewed cost data. Results: Since economic analysis requires estimation in monetary units, the usefulness of transformation-based methods is limited by the inability to retransform cost differences to the original scale. Non-parametric rank sum methods were also found to be of limited use for economic analysis, partly due to the focus on hypothesis testing rather than estimation. Overall, the non-parametric approach of bootstrapping was found to offer a useful test of the appropriateness of parametric assumptions and an alternative method of estimation where those assumptions were found not to hold. Conclusions: Guidelines for the analysis of skewed health care cost data are offered.
Non-parametric heteroscedastic transformation regression models for skewed data with an application to health care costs
