A flexible method for aggregation of prior statistical findings

Rapid growth in scientific output requires methods for quantitative synthesis of prior research, yet current meta-analysis methods limit aggregation to studies with similar designs. Here we describe and validate Generalized Model Aggregation (GMA), which allows researchers to combine prior estimated models of a phenomenon into a quantitative meta-model, while imposing few restrictions on the structure of prior models or on the meta-model. In an empirical validation, building on 27 published equations from 16 studies, GMA provides a predictive equation for Basal Metabolic Rate that outperforms existing models, identifies novel nonlinearities, and estimates biases in various measurement methods. Additional numerical examples demonstrate the ability of GMA to obtain unbiased estimates from potentially mis-specified prior studies. Thus, in various domains, GMA can leverage previous findings to compare alternative theories, advance new models, and assess the reliability of prior studies, extending meta-analysis toolbox to many new problems.科研产出的快速增长亟需可对既往研究进行定量综合的方法，但当前的元分析（meta-analysis）方法仅能聚合研究设计相似的研究。本文描述并验证了广义模型聚合（Generalized Model Aggregation，GMA）方法，该方法允许研究者将针对某一现象的既往估计模型整合为一个定量元模型，且对既往模型结构及元模型本身仅施加极少限制。在一项实证验证中，研究基于16项已发表研究中的27个方程，构建了基础代谢率（Basal Metabolic Rate）的预测方程，该模型性能优于现有模型，可识别出新的非线性关系，并能评估各类测量方法的偏差。额外的数值算例进一步证明，GMA能够从潜在设定有误的既往研究中获取无偏估计量。由此可见，在诸多领域中，GMA可借助既往研究成果对比不同理论、推导新型模型，并评估既往研究的可靠性，从而将元分析工具箱拓展至诸多全新问题场景。