The objective of this paper is to provide an overview of the central issues with respect to cost valuation and analysis for a decision maker’s evaluation of costing performed within randomized clinical trials. Costing involves specific choices for valuation and analysis that involve tradeoffs. Understanding these choices and their implications are necessary for proper evaluation of how costs are valued and analyzed within a randomized clinical trial that can not be assessed through a checklist of adherence to general principals..
The most common method of costing, resource costing, involves measuring medical service use in study case report forms and translating this use into a cost by multiplying the number of units of each medical service by price weights for those services. A choice must be made as to how detailed the measurement of resources will be. Micro-costing improves the specificity of the cost estimate, but it is often impractical to precisely measure resources at this level and the price weights for these micro units may not be available. Gross-costing may be more practical and price weights are often easier to find and are more reliable, but important resource differences between treatment groups may be lost in the bundling of resources. Price weights can be either nationally determined or they can be center-specific, but the appropriate price weight will depend on perspective, convenience, completeness, and accuracy. Identifying the resource types and the appropriate price weights for these resources are the essential elements to an accurate valuation of costs.
Once medical services are valued, the resulting individual patient cost estimates must be analyzed. The difference in the average cost between treatment groups is the important summary statistic for cost-effectiveness analysis both from the budgetary and social perspectives. The statistical challenges with cost data typically stem from its skewed distribution and the resulting possibility that the sample mean may be inefficient and possibly inappropriate for statistical inference. Multivariable analysis of cost is useful even if the data come from a randomized trial, but the same distributional problems that affect univariate tests of cost also affect use of cost as a dependent variable in a multivariable regression analysis. The Generalized Linear Model (GLM) overcomes many of the problems of more common cost models, but one must be cautious when applying this model because it is prone to misspecification and precision losses in data with a heavy-tailed log error term.
Attention to the appropriate cost valuation and analysis techniques reviewed here will help bring the same level of rigor and attention to the methodological issues in cost valuation as currently applied to clinical evidence within randomized trials.