论文标题
收缩估算中外部数据重量的界限
Bounds for the weight of external data in shrinkage estimation
论文作者
论文摘要
荟萃分析框架中的收缩估计可以用于促进信息的动态借贷。根据先前的数据，该框架可以用来分析一项新研究，这些数据可能在其设计方面有所不同（例如，随机对照试验（RCT）和临床注册表）。我们展示了如何在有效的和收缩估计中出现常见研究权重，以及如何将其推广到贝叶斯荟萃分析的情况下。接下来，我们开发简单的方法来计算权重的界限，以便可以先验评估外部证据的贡献。使用数值示例对这些考虑进行了说明和讨论，包括在治疗Creutzfeldt-Jakob疾病和胎儿监测中的应用，以防止发生代谢性酸中毒。目标研究对所得估计值的贡献显示在下面是有限的。因此，很容易被外部数据淹没的证据的担忧在很大程度上是没有根据的。
Shrinkage estimation in a meta-analysis framework may be used to facilitate dynamical borrowing of information. This framework might be used to analyze a new study in the light of previous data, which might differ in their design (e.g., a randomized controlled trial (RCT) and a clinical registry). We show how the common study weights arise in effect and shrinkage estimation, and how these may be generalized to the case of Bayesian meta-analysis. Next we develop simple ways to compute bounds on the weights, so that the contribution of the external evidence may be assessed a priori. These considerations are illustrated and discussed using numerical examples, including applications in the treatment of Creutzfeldt-Jakob disease and in fetal monitoring to prevent the occurrence of metabolic acidosis. The target study's contribution to the resulting estimate is shown to be bounded below. Therefore, concerns of evidence being easily overwhelmed by external data are largely unwarranted.
