Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands (HK, GAdW)
Center for Prevention and Health Services Research, National Institute of Public Health and the Environment, Bilthoven, The Netherlands (GAdW, TLF)
Med Decis Making. 2012 May-Jun;32(3):477-87. doi: 10.1177/0272989X12436725. Epub 2012 Feb 28.
In cost-effectiveness analysis (CEA), it is common to compare a single, new intervention with 1 or more existing interventions representing current practice ignoring other, unrelated interventions. Sectoral CEAs, in contrast, take a perspective in which the costs and effectiveness of all possible interventions within a certain disease area or health care sector are compared to maximize health in a society given resource constraints. Stochastic league tables (SLT) have been developed to represent uncertainty in sectoral CEAs but have 2 shortcomings: 1) the probabilities reflect inclusion of individual interventions and not strategies and 2) data on robustness are lacking. The authors developed an extension of SLT that addresses these shortcomings.
Analogous to nonprobabilistic MAXIMIN decision rules, the uncertainty of the performance of strategies in sectoral CEAs may be judged with respect to worst possible outcomes, in terms of health effects obtainable within a given budget. Therefore, the authors assessed robustness of strategies likely to be optimal by performing optimization separately on all samples and on samples yielding worse than expected health benefits. The approach was tested on 2 examples, 1 with independent and 1 with correlated cost and effect data.
The method was applicable to the original SLT example and to a new example and provided clear and easily interpretable results. Identification of interventions with robust performance as well as the best performing strategies was straightforward. Furthermore, the robustness of strategies was assessed with a MAXIMIN decision rule.
The SLT extension improves the comprehensibility and extends the usefulness of outcomes of SLT for decision makers. Its use is recommended whenever an SLT approach is considered.
在成本效益分析（CEA）中，通常将单一的新干预措施与 1 种或多种代表当前实践的现有干预措施进行比较，而忽略了其他不相关的干预措施。相比之下，部门 CEA 从一个角度出发，即在资源有限的情况下，比较特定疾病领域或医疗保健部门内所有可能的干预措施的成本和效果，以最大限度地提高社会的健康水平。随机联赛表（SLT）已被开发用于表示部门 CEA 中的不确定性，但存在 2 个缺点：1）概率反映了单个干预措施的纳入，而不是策略的纳入；2）缺乏稳健性数据。作者开发了一种扩展的 SLT，以解决这些缺点。
类似于非概率 MAXIMIN 决策规则，可以根据给定预算内可获得的健康效果的最差可能结果，用策略在部门 CEA 中的性能的不确定性进行判断。因此，作者通过在所有样本和产生预期健康效益较差的样本上分别进行优化，评估了可能是最优策略的稳健性。该方法在 2 个示例中进行了测试，1 个示例具有独立的成本和效果数据，1 个示例具有相关的成本和效果数据。
该方法适用于原始 SLT 示例和新示例，并提供了清晰且易于解释的结果。识别具有稳健性能的干预措施以及表现最佳的策略非常简单。此外，还使用 MAXIMIN 决策规则评估了策略的稳健性。
SLT 扩展提高了决策者对 SLT 结果的理解能力和有用性。建议在考虑使用 SLT 方法时使用。
