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In most societies, resources are distributed by individuals acting in markets and by governments through some form of collective decision-making process. Economic evaluation offers a set of tools to inform collective decisions by examining the resource requirements and outcomes of alternative policies. The 'societal perspective' has been advocated, but less consideration has been given to what this should include and its practical implementation. This paper presents a framework for economic evaluation of policies with costs and outcomes falling on different sectors (e.g. health, criminal justice, education) and involving different decision makers. It extends the 'impact inventory' developed by the Second Panel on Cost-Effectiveness in Health and Medicine by considering all affected individuals and reflecting how outcomes attributed to an intervention can be compared with outcomes forgone as a result of resources not being available for other purposes. The framework sets out the series of assessments to be made, distinguishing points at which value judgements feed into the evaluation, and the implications of alternative judgements. These assessments reflect the institutional arrangements of public bodies, for example, their funding, the outcomes they consider important and their relative valuations of these outcomes. By avoiding the use of an abstract 'societal perspective', the contribution of the framework is to inform multiple decision makers with different objectives and provide practical guidance on overall societal impact. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s40258-019-00481-8) contains supplementary material, which is available to authorized users.

AbstractFault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed‐form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling‐based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed‐form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks's method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models.