This article considers Bayesian approaches for incorporating information from a historical model into a current analysis when the historical model includes only a subset of covariates currently of interest. The statistical challenge is 2-fold. First, the parameters in the nested historical model are not generally equal to their counterparts in the larger current model, neither in value nor interpretation. Second, because the historical information will not be equally informative for all parameters in the current analysis, additional regularization may be required beyond that provided by the historical information. We propose several novel extensions of the so-called power prior that adaptively combine a prior based upon the historical information with a variance-reducing prior that shrinks parameter values toward zero. The ideas are directly motivated by our work building mortality risk prediction models for pediatric patients receiving extracorporeal membrane oxygenation (ECMO). We have developed a model on a registry-based cohort of ECMO patients and now seek to expand this model with additional biometric measurements, not available in the registry, collected on a small auxiliary cohort. Our adaptive priors are able to use the information in the original model and identify novel mortality risk factors. We support this with a simulation study, which demonstrates the potential for efficiency gains in estimation under a variety of scenarios.

中文翻译：

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将历史模型与自适应贝叶斯更新相结合。


本文考虑当历史模型仅包含当前感兴趣的协变量的子集时，使用贝叶斯方法将历史模型中的信息合并到当前分析中。统计挑战是双重的。首先，嵌套历史模型中的参数通常不等于较大当前模型中的对应参数，无论是在值上还是在解释上。其次，由于历史信息不会为当前分析中的所有参数提供同等的信息，因此除了历史信息提供的之外，可能还需要额外的正则化。我们提出了所谓的幂先验的几种新颖扩展，自适应地将基于历史信息的先验与将参数值缩小到零的方差减少先验相结合。这些想法的直接灵感来自于我们为接受体外膜肺氧合 (ECMO) 的儿科患者建立死亡风险预测模型的工作。我们已经开发了一个基于登记的 ECMO 患者队列的模型，现在寻求通过在小型辅助队列中收集的登记中不可用的额外生物特征测量来扩展该模型。我们的自适应先验能够使用原始模型中的信息并识别新的死亡风险因素。我们通过模拟研究来支持这一点，该研究展示了在各种情况下提高估计效率的潜力。