利用生成式AI及免編碼工具進行網絡統合分析

A Guide to Network Meta-Analysis Using Generative AI and No-Code Tools

10.6224/JN.202410_71(5).05

劉人瑋(Jen-Wei LIU)

網絡統合分析 ； 生成式AI ； ChatGPT ； network meta-analysis ； generative artificial intelligence ； ChatGPT

護理雜誌

71卷5期（2024 / 10 / 01）

29 - 35

繁體中文;英文

網絡統合分析（network meta-analysis, NMA）已成為一項吸引人的統計分析方法，相較於傳統分析方式，NMA可以在同一個分析中同時比較多種治療方法。近年來，它在醫學文獻中的普及率大幅提高，並伴隨著統計方法和軟體工具的不斷演進。各種商業和免費的統計軟體套件已被開發出來，以進行NMA分析，包括利用生成式人工智慧（generative artificial intelligence, GAI）產生程式碼，有許多創新發展。因此本文旨在簡介如何使用生成式AI撰寫R程式語言，並利用netmeta套件進行NMA分析；並介紹一款基於網頁為基礎開發的工具ShinyNMA，僅需一般網路瀏覽器，使用者即可透過直觀的「點擊式」介面進行NMA，並以視覺化圖表呈現結果。第一部分，參考netmeta套件說明文件，並利用ChatGPT（chat generative pre-trained transformer）撰寫R語言搭配netmeta套件進行NMA分析；第二部分則利用Shiny套件開發使用者介面，開發ShinyNMA免編碼工具，提供不熟悉編碼的使用者另一個進行NMA統計分析及繪圖的選項。以適當提詞，ChatGPT可以產出可進行NMA的R語言；同時，我們成功開發一個滿足研究目標的NMA分析工具，並透過示範資料展示這個工具可能的應用。我們相信，透過生成式AI及現有統計套件，或是免編碼工具將有助於廣大研究人員更輕鬆地進行NMA分析，同時使決策者能夠即時直觀地審視分析結果。這將提升統計分析在醫療決策中的重要性，並透過讓非專業人士進行更具臨床意義的系統性回顧，可持續提升分析能力，產出高品質證據。

Network meta-analysis (NMA), an increasingly appealing method of statistical analysis, is superior to traditional analysis methods in terms of being able to compare multiple medical treatment methods in one analysis run. In recent years, the prevalence of NMA in the medical literature has increased significantly, while advances in NMA-related statistical methods and software tools continue to improve the effectiveness of this approach. Various commercial and free statistical software packages, some of which employ generative artificial intelligence (GAI) to generate code, have been developed for NMA, leading to numerous innovative developments. In this paper, the use of generative AI for writing R programming language scripts and the netmeta package for performing NMA are introduced. Also, the web-based tool ShinyNMA is introduced. ShinyNMA allows users to conduct NMA using an intuitive "clickable" interface accessible via a standard web browser, with visual charts employed to present results. In the first section, we detail the netmeta package documentation and use ChatGPT (chat generative pre-trained transformer) to write the R scripts necessary to perform NMA with the netmeta package. In the second section, a user interface is developed using the Shiny package to create a ShinyNMA tool. This tool provides a no-code option for users unfamiliar with programming to conduct NMA statistical analysis and plotting. With appropriate prompts, ChatGPT can produce R scripts capable of performing NMA. Using this approach, an NMA analysis tool is developed that meets the research objectives, and potential applications are demonstrated using sample data. Using generative AI and existing statistical packages or no-code tools is expected to make conducting NMA analysis significantly easier for researchers. Moreover, greater access to results generated by NMA analyses will enable decision-makers to review analysis results intuitively in real-time, enhancing the importance of statistical analysis in medical decision-making. Furthermore, enabling non-specialists to conduct clinically meaningful systematic reviews may be expected to sustainably improve analytical capabilities and produce higher-quality evidence.