title: The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies.
abstract: The literature which considers the statistical properties of cost-effectiveness analysis has focused on estimating the sampling distribution of either an incremental cost-effectiveness ratio or incremental net benefit for classical inference. However, it is argued here that rules of inference are arbitrary and entirely irrelevant to the decisions which clinical and economic evaluations claim to inform. Decisions should be based only on the mean net benefits irrespective of whether differences are statistically significant or fall outside a Bayesian range of equivalence. Failure to make decisions in this way by accepting the arbitrary rules of inference will impose costs which can be measured in terms of resources or health benefits forgone. The distribution of net benefit is only relevant to deciding whether more information is required. A framework for decision making and establishing the value of additional information is presented which is consistent with the decision rules in CEA. This framework can distinguish the simultaneous but conceptually separate steps of deciding which alternatives should be chosen, given existing information, from the question of whether more information should be acquired. It also ensures that the type of information acquired is driven by the objectives of the health care system, is consistent with the budget constraint on service provision and that research is designed efficiently.
authors.affiliation: Commonwealth Fund of New York, Harvard Center for Risk Analysis, Harvard School of Public Health, Boston, MA, USA. kclaxton@hsph.harvard.edu
authors.forename: K
authors.initials: K
authors.lastname: Claxton
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D003362:Cost-Benefit Analysis / Q000706:statistics & numerical data*; D003657:Decision Making; D017048:Health Care Costs / Q000706:statistics & numerical data; D006801:Humans; D017059:Models, Econometric; D013269:Stochastic Processes; D013673:Technology Assessment, Biomedical / Q000191:economics* / Q000379:methods; D006113:United Kingdom