荟萃分析加速器:系统综述与荟萃分析中统计数据转换的综合工具。

IF 3.9 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES BMC Medical Research Methodology Pub Date : 2024-10-18 DOI:10.1186/s12874-024-02356-6
Abdallah Abbas, Mahmoud Tarek Hefnawy, Ahmed Negida
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引用次数: 0

摘要

背景:系统综述与荟萃分析整合了多项研究的结果,为治疗效果提供了可靠的结论,并为循证医学提供了指导。然而,这一过程往往受到数据报告不一致、计算复杂和时间限制等挑战的阻碍。研究人员必须将各种统计量转换成通用格式,如果没有合适的工具,这可能是一个容易出错和劳动密集型的过程:元分析加速器就是为应对这些挑战而开发的。该工具提供 21 种不同的统计转换,包括将中位数和四分位数间距 (IQR) 转换为平均值和标准差 (SD),将平均值的标准误差 (SEM) 转换为 SD,以及将一组和两组的置信区间 (CI) 转换为 SD 等。它采用直观的界面设计,确保用户可以轻松浏览该工具,并准确高效地进行转换。网站结构包括主页、转换页面、请求转换功能、关于页面、文章页面和隐私政策页面。这种全面的设计支持了该工具简化荟萃分析流程的主要目标:自 2023 年 10 月作为 Meta 转换器首次发布并随后更名为 Meta 分析加速器以来,该工具已在全球范围内得到广泛使用。从 2024 年 3 月到 2024 年 5 月,该工具收到了来自埃及、法国、印度尼西亚和美国等国家的 12,236 次访问,显示了其国际吸引力和实用性。约 46% 的访问是直接访问,这反映了它在用户中的受欢迎程度和信任度:Meta-Analysis Accelerator 通过提供可靠的统计数据转换平台,大大提高了系统综述荟萃分析的效率和准确性。它的转换种类齐全,界面友好,而且还在不断改进,是研究人员不可或缺的资源。该工具能够简化数据转换,确保研究人员能够将更多精力放在数据解读上,减少手工计算,从而提高系统综述和荟萃分析的质量和易用性。
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Meta-analysis accelerator: a comprehensive tool for statistical data conversion in systematic reviews with meta-analysis.

Background: Systematic review with meta-analysis integrates findings from multiple studies, offering robust conclusions on treatment effects and guiding evidence-based medicine. However, the process is often hampered by challenges such as inconsistent data reporting, complex calculations, and time constraints. Researchers must convert various statistical measures into a common format, which can be error-prone and labor-intensive without the right tools.

Implementation: Meta-Analysis Accelerator was developed to address these challenges. The tool offers 21 different statistical conversions, including median & interquartile range (IQR) to mean & standard deviation (SD), standard error of the mean (SEM) to SD, and confidence interval (CI) to SD for one and two groups, among others. It is designed with an intuitive interface, ensuring that users can navigate the tool easily and perform conversions accurately and efficiently. The website structure includes a home page, conversion page, request a conversion feature, about page, articles page, and privacy policy page. This comprehensive design supports the tool's primary goal of simplifying the meta-analysis process.

Results: Since its initial release in October 2023 as Meta Converter and subsequent renaming to Meta-Analysis Accelerator, the tool has gained widespread use globally. From March 2024 to May 2024, it received 12,236 visits from countries such as Egypt, France, Indonesia, and the USA, indicating its international appeal and utility. Approximately 46% of the visits were direct, reflecting its popularity and trust among users.

Conclusions: Meta-Analysis Accelerator significantly enhances the efficiency and accuracy of meta-analysis of systematic reviews by providing a reliable platform for statistical data conversion. Its comprehensive variety of conversions, user-friendly interface, and continuous improvements make it an indispensable resource for researchers. The tool's ability to streamline data transformation ensures that researchers can focus more on data interpretation and less on manual calculations, thus advancing the quality and ease of conducting systematic reviews and meta-analyses.

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来源期刊
BMC Medical Research Methodology
BMC Medical Research Methodology 医学-卫生保健
CiteScore
6.50
自引率
2.50%
发文量
298
审稿时长
3-8 weeks
期刊介绍: BMC Medical Research Methodology is an open access journal publishing original peer-reviewed research articles in methodological approaches to healthcare research. Articles on the methodology of epidemiological research, clinical trials and meta-analysis/systematic review are particularly encouraged, as are empirical studies of the associations between choice of methodology and study outcomes. BMC Medical Research Methodology does not aim to publish articles describing scientific methods or techniques: these should be directed to the BMC journal covering the relevant biomedical subject area.
期刊最新文献
Non-collapsibility and built-in selection bias of period-specific and conventional hazard ratio in randomized controlled trials. Exploring the characteristics, methods and reporting of systematic reviews with meta-analyses of time-to-event outcomes: a meta-epidemiological study. The role of the estimand framework in the analysis of patient-reported outcomes in single-arm trials: a case study in oncology. Cardinality matching versus propensity score matching for addressing cluster-level residual confounding in implantable medical device and surgical epidemiology: a parametric and plasmode simulation study. Establishing a machine learning dementia progression prediction model with multiple integrated data.
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