Australian Women and Girls' Health Research Centre, School of Public Health, Faculty of Medicine, The University of Queensland, Level 3 Public Health Building, 288 Herston Rd, Herston, QLD, 4006, Australia.
BMC Med Res Methodol. 2024 Jul 23;24(1):157. doi: 10.1186/s12874-024-02286-3.
Network analysis, commonly used to describe the patterns of multimorbidity, uses the strength of association between conditions as weight to classify conditions into communities and calculate centrality statistics. Our aim was to examine the robustness of the results to the choice of weight.
Data used on 27 chronic conditions listed on Australian death certificates for women aged 85+. Five statistics were calculated to measure the association between 351 possible pairs: odds ratio (OR), lift, phi correlation, Salton cosine index (SCI), and normalised-joint frequency of pairs (NF). Network analysis was performed on the 10% of pairs with the highest weight according to each definition, the 'top pairs'.
Out of 56 'top pairs' identified, 13 ones were consistent across all statistics. In networks of OR and lift, three of the conditions which did not join communities were among the top five most prevalent conditions. Networks based on phi and NF had one or two conditions not part of any community. For the SCI statistics, all three conditions which did not join communities had prevalence below 3%. Low prevalence conditions were more likely to have high degree in networks of OR and lift but not SCI.
Use of different statistics to estimate weights leads to different networks. For exploratory purposes, one may apply alternative weights to identify a large list of pairs for further assessment in independent studies. However, when the aim is to visualise the data in a robust and parsimonious network, only pairs which are selected by multiple statistics should be visualised.
网络分析常用于描述多种疾病模式，使用疾病之间的关联强度作为权重，将疾病分类为社区，并计算中心性统计量。我们的目的是检验权重选择对结果的稳健性。
使用澳大利亚女性 85 岁以上死亡证明上列出的 27 种慢性疾病的数据。计算了 351 对可能配对的 5 种关联统计量：比值比（OR）、提升度、phi 相关系数、Salton 余弦指数（SCI）和配对的归一化联合频率（NF）。根据每个定义，对权重最高的 10%的配对（即“top pairs”）进行网络分析。
在确定的 56 对“top pairs”中，有 13 对在所有统计量中都是一致的。在 OR 和 lift 的网络中，三个未加入社区的条件是最常见的五种条件之一。基于 phi 和 NF 的网络中，有一个或两个条件不属于任何社区。对于 SCI 统计量，三个未加入社区的条件的患病率均低于 3%。低患病率的条件在 OR 和 lift 的网络中更有可能具有高度数，但在 SCI 中则不然。
使用不同的统计量来估计权重会导致不同的网络。对于探索性目的，可以使用替代权重来识别大量配对，以便在独立研究中进一步评估。但是，当目的是在一个稳健且简约的网络中可视化数据时，只有被多个统计量选择的配对才应被可视化。
