Cost-effectiveness Analysis, Cost-Benefit Analysis
Although a treatment or screening technique may be shown to be effective in an RCT, recommending it in general practice would not necessarily be rational. Such interventions may be prohibitively expensive, or they may benefit only a small number of people at the expense of a large number of people, or they may lead to significant “downstream” costs that would eventually negate any immediate savings or benefit. Thus, it is possible that interventions that appear less effective may actually lead to the greatest societal benefits over the long term.
Cost-effectiveness and cost-benefit analyses comprise a set of mathematical techniques to model these complex consequences of medical interventions. Cost-effectiveness analysis “compares the net monetary costs of a health care intervention with some measure of clinical outcome or effectiveness such as mortality rates or life-years saved.”26 Cost-benefit analysis is similar but converts clinical measures of outcomes into monetary units, allowing both costs and benefits to be expressed on a single scale. This use of a common metric thus enables comparisons between different treatment or screening strategies.
The results of a cost-effectiveness analysis are usually expressed in terms of a cost-effectiveness ratio, for example, the cost per year of life gained. The use of quality-adjusted life-years (QALYs) or disability-adjusted life-years (DALYs) permits direct comparison of different types of interventions using the same measure for outcomes. The use of such composite measures allows researchers to weigh the relative benefits of length and quality of life.
The complexity of these analyses and the many decisions required when selecting data and choosing assumptions may be of particular concern when the analysis is performed by an investigator or company with financial interest in the treatment being evaluated.27 Such analyses may have biases that are difficult to detect even with the most rigorous peer review process.28
One approach frequently used by cost-effectiveness analysts is to define a base case that represents the choices to be considered, perform an analysis for the base case, and then perform sensitivity analyses to determine how varying the data used and assumptions made for the base case affects the results. Sometimes authors test their conclusions by performing bootstrap or jackknife analyses. This involves taking a very large number of repeated random samples from the data and then observing whether this procedure generally replicates the previous analytic conclusions. A number of journals have published guidelines and approaches to cost-effectiveness analysis, but consensus has yet to emerge on their reporting29-32 or interpretation.33 Nonetheless, authors should clearly indicate all sources of data for both treatment effects and costs. Graphical approaches may help readers better understand the basic conclusions of the analysis.34 JAMA requires authors of cost-effectiveness analyses and decision analyses to submit a copy of the decision tree comprising their model. Although this need not necessarily be included in the body of the published article, such information is necessary for reviewers and editors to assess the details of the model and its analysis.