We introduce and develop a general paradigm for combining information across diverse data sources. In broad terms, suppose $φ$ is a parameter of interest, built up via components $ψ_1,\ldots,ψ_k$ from data sources $1,\ldots,k$. The proposed scheme has three steps. First, the Independent Inspection (II) step amounts to investigating each separate data source, translating statistical information to a confidence distribution $C_j(ψ_j)$ for the relevant focus parameter $ψ_j$ associated with data source $j$. Second, Confidence Conversion (CC) techniques are used to translate the confidence distributions to confidence log-likelihood functions, say $\ell_{{\rm con},j}(ψ_j)$. Finally, the Focused Fusion (FF) step uses relevant and context-driven techniques to construct a confidence distribution for the primary focus parameter $φ=φ(ψ_1,\ldots,ψ_k)$, acting on the combined confidence log-likelihood. In traditional setups, the II-CC-FF strategy amounts to versions of meta-analysis, and turns out to be competitive against state-of-the-art methods. Its potential lies in applications to harder problems, however. Illustrations are presented, related to actual applications.

翻译：本文提出并发展了一种融合多元数据源信息的通用范式。简而言之，假设目标参数$φ$由来自数据源$1,\ldots,k$的分量$ψ_1,\ldots,ψ_k$构成。所提方案包含三个步骤：首先，在独立检验（II）步骤中，对各独立数据源进行分析，将统计信息转化为对应数据源$j$中相关焦点参数$ψ_j$的置信分布$C_j(ψ_j)$；其次，通过置信转换（CC）技术将置信分布转化为置信对数似然函数$\ell_{{\rm con},j}(ψ_j)$；最后，在聚焦融合（FF）步骤中，基于组合后的置信对数似然，运用与上下文相关的技术构建主要焦点参数$φ=φ(ψ_1,\ldots,ψ_k)$的置信分布。在传统研究框架下，II-CC-FF策略可视为元分析的变体，其性能与前沿方法相当。然而，该范式的潜力主要体现在处理更复杂问题的应用中。本文结合实际应用案例进行了方法演示。