Brunson Jason Cory, Agresta Thomas P, Laubenbacher Reinhard C
Center for Quantitative Medicine, UConn Health, 263 Farmington Ave, Farmington, Connecticut 06030-6033, USA.
Department of Family Medicine, UConn Health, 263 Farmington Ave, Farmington, Connecticut 06030-6033, USA.
JAMIA Open. 2019 Dec 31;3(1):94-103. doi: 10.1093/jamiaopen/ooz067. eCollection 2020 Apr.
Comorbidity network analysis (CNA) is a graph-theoretic approach to systems medicine based on associations revealed from disease co-occurrence data. Researchers have used CNA to explore epidemiological patterns, differentiate populations, characterize disorders, and more; but these techniques have not been comprehensively evaluated. Our objectives were to assess the stability of common CNA techniques.
We obtained seven co-occurrence data sets, most from previous CNAs, coded using several ontologies. We constructed comorbidity networks under various modeling procedures and calculated summary statistics and centrality rankings. We used regression, ordination, and rank correlation to assess these properties' sensitivity to the source of data and construction parameters.
Most summary statistics were robust to variation in link determination but somewhere sensitive to the association measure. Some more effectively than others discriminated among networks constructed from different data sets. Centrality rankings, especially among hubs, were somewhat sensitive to link determination and highly sensitive to ontology. As multivariate models incorporated additional effects, comorbid associations among low-prevalence disorders weakened while those between high-prevalence disorders shifted negative.
Pairwise CNA techniques are generally robust, but some analyses are highly sensitive to certain parameters. Multivariate approaches expose additional conceptual and technical limitations to the usual pairwise approach.
We conclude with a set of recommendations we believe will help CNA researchers improve the robustness of results and the potential of follow-up research.
共病网络分析（CNA）是一种基于疾病共现数据所揭示的关联关系的系统医学的图论方法。研究人员已使用CNA来探索流行病学模式、区分人群、刻画疾病特征等；但这些技术尚未得到全面评估。我们的目标是评估常用CNA技术的稳定性。
我们获取了七个共现数据集，大部分来自先前的CNA研究，使用多种本体进行编码。我们在各种建模程序下构建共病网络，并计算汇总统计量和中心性排名。我们使用回归、排序和秩相关来评估这些属性对数据来源和构建参数的敏感性。
大多数汇总统计量对链接确定的变化具有稳健性，但在某种程度上对关联度量敏感。一些统计量在区分由不同数据集构建的网络方面比其他统计量更有效。中心性排名，尤其是枢纽节点之间的排名，对链接确定有些敏感，对本体高度敏感。随着多变量模型纳入更多效应，低患病率疾病之间的共病关联减弱，而高患病率疾病之间的共病关联变为负相关。
成对CNA技术通常具有稳健性，但某些分析对特定参数高度敏感。多变量方法揭示了常规成对方法存在的额外概念和技术局限性。
我们最后给出了一系列建议，我们相信这些建议将有助于CNA研究人员提高结果的稳健性以及后续研究的潜力。
