Objectives: The aim of this paper is to contrast the retrospective and prospective use of experts beliefs in choosing between survival models in economic evaluations. Methods: The use of experts retrospective (posterior) beliefs is discussed. A process for prospectively quantifying prior beliefs about model parameters in five standard models is described. Statistical criterion for comparing models, and the interpretation and computation of model probabilities is discussed. A case study is provided. Results: Experts have little difficulty in expressing their posterior beliefs. Information criterion is an approximation to Bayesian model evidence and is based on data alone. In contrast, Bayes factors measure evidence in the data and makes use of prior information. When model averaging is of interest, there is no unique way to specify prior ignorance about model probabilities. Formulating and interpreting weights of similar models should acknowledge the dilution phenomenon such that highly correlated models are given smaller weights than those with low correlation. Conclusion: The retrospective use of experts beliefs to validate a model is potentially misleading, may not achieve its intended objective and is an inefficient use of information. Experts beliefs should be elicited prospectively as probability distributions to strengthen inferences, facilitate the choice of model, and mitigate the impact of dilution on model probabilities in situations when model averaging is of interest.
翻译:本文件的目的是比较在经济评估中选择生存模式时专家信仰的追溯性和预期用途; 方法: 专家回顾(前)信仰的使用; 讨论对五个标准模型中模型参数的先前信仰进行预期量化的过程; 讨论比较模型的统计标准以及模型概率的解释和计算; 提供案例研究; 结果:专家在表达其后背信仰方面几乎没有多少困难; 信息标准是巴耶斯模型证据的近似值,仅以数据为基础; 相比之下,贝耶斯因素衡量数据中的证据,并使用先前的信息; 当平均模型引起兴趣时,没有独特的方法来说明以前对模型概率的无知; 拟订和解释类似模型的权重应当承认稀释现象,即高度关联模型的权重比低; 结论:追溯利用专家信念验证模型可能具有误导性,可能无法实现其预期目标,而且信息使用效率低下; 专家信念应当被预期为概率分布模型,以加强模型概率的概率,当选择模型时,有助于降低模型对稳定性的影响; 减轻模型的影响; 降低模型对稳定性的影响; 降低模型的影响; 降低模型的影响; 降低模型对稳定性的影响; 降低影响; 降低利率。