Department of Business Economics, Health and Social Care (DEASS), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), Manno, Ticino, Switzerland.
BayesCamp Ltd, Winchester, UK.
Pharmacoeconomics. 2024 Jul;42(7):721-735. doi: 10.1007/s40273-024-01387-7. Epub 2024 May 20.
Researchers incorporate health state utility values as inputs to inform economic models. However, for a particular health state or condition, multiple utility values derived from different studies typically exist and a single study is often insufficient to represent the best available source of utility needed to inform policy decisions. The purpose of this paper is to provide an introductory guidance for conducting Bayesian meta-analysis of health state utility values to generate a single parameter input for economic evaluation, using R. The tutorial is illustrated using data from a systematic review of health state utilities of patients with heart failure, with 21 studies that reported utilities measured using the EuroQol-5D (EQ-5D). Explanations, key considerations and suggested readings are provided for each step of the tutorial, adhering to a clear workflow for conducting Bayesian meta-analysis: (1) setting-up the data; (2) employing methods to impute missing standard deviations; (3) defining the priors; (4) fitting the model; (5) diagnosing model convergence; (6) interpreting the results; and (7) performing sensitivity analyses. The posterior distributions for the pooled effect size (i.e. mean health state utility) and between-study heterogeneity are discussed and interpreted in light of the data, priors and models used. We hope that this tutorial will foster interest in Bayesian methods and their applications in the meta-analysis of utilities.
研究人员将健康状态效用值作为输入纳入经济模型。然而,对于特定的健康状态或状况,通常存在多个来自不同研究的效用值,并且单个研究通常不足以代表为政策决策提供信息所需的最佳效用来源。本文的目的是提供使用 R 对健康状态效用值进行贝叶斯荟萃分析的入门指导,以生成经济评估的单一参数输入。本教程使用来自心力衰竭患者健康状态效用的系统评价的 21 项研究报告的效用数据进行说明,这些研究使用 EuroQol-5D (EQ-5D) 进行了测量。本教程为贝叶斯荟萃分析的每个步骤提供了解释、关键考虑因素和建议阅读材料,遵循进行贝叶斯荟萃分析的明确工作流程:(1) 设置数据;(2) 使用方法估算缺失的标准偏差;(3) 定义先验;(4) 拟合模型;(5) 诊断模型收敛;(6) 解释结果;(7) 进行敏感性分析。根据使用的数据、先验和模型,讨论和解释了汇总效应量(即平均健康状态效用)和研究间异质性的后验分布。我们希望本教程将激发对贝叶斯方法及其在效用荟萃分析中的应用的兴趣。