Combinational Regularity Analysis (CORA) - A New Method for Uncovering Complex Causation in Medical and Health Research

Background: Modern configurational comparative methods (CCMs) of causal inference, such as Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), have started to make inroads into medical and health research over the last decade. At the same time, these methods remain unable to process data on multi-morbidity, a situation in which at least two chronic conditions are simultaneously present. Such data require the capability to analyze complex effects. Against a background of fast-growing numbers of patients with multi-morbid diagnoses, we present a new member of the family of CCMs with which multiple conditions and their complex conjunctions can be analyzed: Combinational Regularity Analysis (CORA).
Methods: The technical heart of CORA consists of algorithms that have originally been developed in electrical engineering for the analysis of multi-output switching circuits. We have adapted these algorithms for purposes of configurational data analysis. To demonstrate CORA, we provide several example applications, both with simulated and empirical data, by means of the eponymous software package CORA. Also included in CORA is the possibility to mine configurational data and to visualize results via logic diagrams.
Results: For simple single-condition analyses, CORA’s solution is identical with that of QCA or CNA. However, analyses of multiple conditions with CORA differ in important respects from analyses with QCA or CNA. Most importantly, CORA is presently the only configurational method able to simultaneously explain individual conditions as well as complex conjunctions of conditions.
Conclusions: Through CORA, problems of multi-morbidity in particular, and configurational analyses of complex effects in general, come into the analytical reach of CCMs. Future research aims to further broaden and enhance CORA’s capabilities for refining such analyses.

背景：在过去的十年中，现代因果推理的配置比较方法（CCMs），如定性比较分析（QCA）和巧合分析（CNA），已经开始渗透到医学和健康研究领域。与此同时，这些方法仍然无法处理多发病症数据，在此情况下，至少存在两种慢性疾病同时存在。此类数据需要具备分析复杂效应的能力。在多发病症诊断患者数量迅速增长的背景下，我们提出了一种新的配置比较方法（CCM）家族成员，可以用来分析多种条件和它们的复杂结合：组合规律分析（CORA）。
方法：CORA的技术核心由最初为分析多输出切换电路而开发的电气工程算法构成。我们已将这些算法改编用于配置数据分析。为了展示CORA，我们通过同名的软件包CORA提供了几个示例应用，包括模拟数据和实证数据。CORA还包括挖掘配置数据并利用逻辑图可视化结果的功能。
结果：对于简单的单条件分析，CORA的解决方案与QCA或CNA相同。然而，使用CORA对多个条件的分析在重要方面与使用QCA或CNA的分析存在差异。最重要的是，CORA目前是唯一能够同时解释单个条件和条件复杂结合的配置方法。
结论：通过CORA，特别是多发病症问题，以及复杂效应的配置分析在一般意义上，均进入了配置比较方法（CCM）的分析范畴。未来的研究旨在进一步拓宽和增强CORA的分析能力。