DTAmetasa: An R shiny application for meta-analysis of diagnostic test accuracy and sensitivity analysis of publication bias

IF 5 2区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Research Synthesis Methods Pub Date : 2023-08-28 DOI:10.1002/jrsm.1666

Shosuke Mizutani, Yi Zhou, Yu-Shi Tian, Tatsuya Takagi, Tadayasu Ohkubo, Satoshi Hattori

{"title":"DTAmetasa: An R shiny application for meta-analysis of diagnostic test accuracy and sensitivity analysis of publication bias","authors":"Shosuke Mizutani, Yi Zhou, Yu-Shi Tian, Tatsuya Takagi, Tadayasu Ohkubo, Satoshi Hattori","doi":"10.1002/jrsm.1666","DOIUrl":null,"url":null,"abstract":"<p>Meta-analysis of diagnostic test accuracy (DTA) is a powerful statistical method for synthesizing and evaluating the diagnostic capacity of medical tests and has been extensively used by clinical physicians and healthcare decision-makers. However, publication bias (PB) threatens the validity of meta-analysis of DTA. Some statistical methods have been developed to deal with PB in meta-analysis of DTA, but implementing these methods requires high-level statistical knowledge and programming skill. To assist non-technical users in running most routines in meta-analysis of DTA and handling with PB, we developed an interactive application, DTAmetasa. DTAmetasa is developed as a web-based graphical user interface based on the R shiny framework. It allows users to upload data and conduct meta-analysis of DTA by “point and click” operations. Moreover, DTAmetasa provides the sensitivity analysis of PB and presents the graphical results to evaluate the magnitude of the PB under various publication mechanisms. In this study, we introduce the functionalities of DTAmetasa and use the real-world meta-analysis to show its capacity for dealing with PB.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Synthesis Methods","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jrsm.1666","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Meta-analysis of diagnostic test accuracy (DTA) is a powerful statistical method for synthesizing and evaluating the diagnostic capacity of medical tests and has been extensively used by clinical physicians and healthcare decision-makers. However, publication bias (PB) threatens the validity of meta-analysis of DTA. Some statistical methods have been developed to deal with PB in meta-analysis of DTA, but implementing these methods requires high-level statistical knowledge and programming skill. To assist non-technical users in running most routines in meta-analysis of DTA and handling with PB, we developed an interactive application, DTAmetasa. DTAmetasa is developed as a web-based graphical user interface based on the R shiny framework. It allows users to upload data and conduct meta-analysis of DTA by “point and click” operations. Moreover, DTAmetasa provides the sensitivity analysis of PB and presents the graphical results to evaluate the magnitude of the PB under various publication mechanisms. In this study, we introduce the functionalities of DTAmetasa and use the real-world meta-analysis to show its capacity for dealing with PB.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

DTAmetasa:用于诊断测试准确性和发表偏倚敏感性分析的meta分析。

诊断测试准确性荟萃分析（DTA）是一种综合和评估医学测试诊断能力的强大统计方法，已被临床医生和医疗决策者广泛使用。然而，发表偏倚（PB）威胁着DTA荟萃分析的有效性。在DTA的荟萃分析中，已经开发了一些统计方法来处理PB，但实施这些方法需要高水平的统计知识和编程技能。为了帮助非技术用户运行DTA荟萃分析中的大多数例程并处理PB，我们开发了一个交互式应用程序DTAetasa。DTAmetasa是基于R闪亮框架开发的基于web的图形用户界面。它允许用户通过“点击”操作上传数据并对DTA进行荟萃分析。此外，DTAmetasa提供了PB的敏感性分析，并提供了图形结果，以评估在各种出版机制下PB的大小。在这项研究中，我们介绍了DTAmetasa的功能，并使用真实世界的荟萃分析来展示其处理PB的能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES

CiteScore

16.90

自引率

3.10%

发文量

期刊介绍： Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.