PRONA: an R-package for Patient Reported Outcomes Network Analysis.

Brandon H Bergsneider, Orieta Celiku
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Abstract

Summary: Network analysis (NA) has recently emerged as a new paradigm by which to model the symptom patterns of patients with complex illnesses such as cancer. NA uses graph theory-based methods to capture the interplay between symptoms and identify which symptoms may be most impactful to patient quality of life and are therefore most critical to treat/prevent. Despite NA's increasing popularity in research settings, its clinical applicability is hindered by the lack of a unified platform that consolidates all the software tools needed to perform NA, and by the lack of methods for capturing heterogeneity across patient cohorts. Addressing these limitations, we present PRONA, an R-package for Patient Reported Outcomes Network Analysis. PRONA not only consolidates previous NA tools into a unified, easy-to-use analysis pipeline, but also augments the traditional approach with functionality for performing unsupervised discovery of patient subgroups with distinct symptom patterns.

Availability and implementation: PRONA is implemented in R. Source code, installation, and use instructions are available on GitHub at https://github.com/bbergsneider/PRONA.

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PRONA:用于患者报告结果网络分析的 R 软件包。
摘要:网络分析(NA)是最近出现的一种新范式,可用于模拟癌症等复杂疾病患者的症状模式。网络分析使用基于图论的方法来捕捉症状之间的相互作用,并确定哪些症状可能对患者的生活质量影响最大,因此是治疗/预防的关键。尽管 NA 在研究环境中越来越受欢迎,但由于缺乏一个统一的平台来整合执行 NA 所需的所有软件工具,以及缺乏捕捉患者队列间异质性的方法,NA 的临床适用性受到了阻碍。为了解决这些局限性,我们推出了 PRONA,一个用于患者报告结果网络分析的 R 软件包。PRONA 不仅将之前的 NA 工具整合到一个统一、易用的分析管道中,还通过对具有不同症状模式的患者亚群进行无监督发现的功能增强了传统方法:PRONA用R语言实现。源代码、安装和使用说明可在GitHub上获取:https://github.com/bbergsneider/PRONA.Supplementary information:补充信息可在 Bioinformatics online 上获取。
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