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开发一个动态交互式网络工具,以增强对多状态模型分析的理解:MSMplus。

Development of a dynamic interactive web tool to enhance understanding of multi-state model analyses: MSMplus.

机构信息

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, Stockholm, Sweden.

Biostatistics Research Group, Department of Health Sciences, University of Leicester, University Road, Leicester, UK.

出版信息

BMC Med Res Methodol. 2021 Nov 27;21(1):262. doi: 10.1186/s12874-021-01420-9.

Abstract

BACKGROUND

Multi-state models are used in complex disease pathways to describe a process where an individual moves from one state to the next, taking into account competing states during each transition. In a multi-state setting, there are various measures to be estimated that are of great epidemiological importance. However, increased complexity of the multi-state setting and predictions over time for individuals with different covariate patterns may lead to increased difficulty in communicating the estimated measures. The need for easy and meaningful communication of the analysis results motivated the development of a web tool to address these issues.

RESULTS

MSMplus is a publicly available web tool, developed via the Shiny R package, with the aim of enhancing the understanding of multi-state model analyses results. The results from any multi-state model analysis are uploaded to the application in a pre-specified format. Through a variety of user-tailored interactive graphs, the application contributes to an improvement in communication, reporting and interpretation of multi-state analysis results as well as comparison between different approaches. The predicted measures that can be supported by MSMplus include, among others, the transition probabilities, the transition intensity rates, the length of stay in each state, the probability of ever visiting a state and user defined measures. Representation of differences, ratios and confidence intervals of the aforementioned measures are also supported. MSMplus is a useful tool that enhances communication and understanding of multi-state model analyses results.

CONCLUSIONS

Further use and development of web tools should be encouraged in the future as a means to communicate scientific research.

摘要

背景

多状态模型用于复杂疾病途径,描述个体从一个状态转移到下一个状态的过程,同时考虑到每个状态转移期间的竞争状态。在多状态环境中,有各种需要估计的措施,这些措施具有重要的流行病学意义。然而,多状态环境的复杂性增加以及对具有不同协变量模式的个体进行随时间的预测可能会导致估计措施的沟通变得更加困难。为了便于轻松和有意义地传达分析结果,开发了一个网络工具来解决这些问题。

结果

MSMplus 是一个公共可用的网络工具,通过 Shiny R 包开发,旨在增强对多状态模型分析结果的理解。任何多状态模型分析的结果都以上述规定的格式上传到应用程序中。通过各种用户定制的交互式图形,该应用程序有助于改善多状态分析结果的沟通、报告和解释,以及不同方法之间的比较。MSMplus 可以支持的预测措施包括但不限于转移概率、转移强度率、在每个状态下的停留时间、访问状态的概率和用户定义的措施。还支持上述措施的差异、比率和置信区间的表示。MSMplus 是一个有用的工具,增强了对多状态模型分析结果的沟通和理解。

结论

未来应鼓励进一步使用和开发网络工具,作为交流科学研究的一种手段。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b268/8627614/d52d42e85cdb/12874_2021_1420_Fig1_HTML.jpg

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