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使用 fragility 包在 R 中评估和可视化二项结果临床结果的脆弱性。

Assessing and visualizing fragility of clinical results with binary outcomes in R using the fragility package.

机构信息

Department of Statistics, Florida State University, Tallahassee, FL, United States of America.

Statistical Research and Innovation, Global Biometrics and Data Management, Pfizer Inc., New York, NY, United States of America.

出版信息

PLoS One. 2022 Jun 1;17(6):e0268754. doi: 10.1371/journal.pone.0268754. eCollection 2022.

DOI:10.1371/journal.pone.0268754
PMID:35648746
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9159630/
Abstract

With the growing concerns about research reproducibility and replicability, the assessment of scientific results' fragility (or robustness) has been of increasing interest. The fragility index was proposed to quantify the robustness of statistical significance of clinical studies with binary outcomes. It is defined as the minimal event status modifications that can alter statistical significance. It helps clinicians evaluate the reliability of the conclusions. Many factors may affect the fragility index, including the treatment groups in which event status is modified, the statistical methods used for testing for the association between treatments and outcomes, and the pre-specified significance level. In addition to assessing the fragility of individual studies, the fragility index was recently extended to both conventional pairwise meta-analyses and network meta-analyses of multiple treatment comparisons. It is not straightforward for clinicians to calculate these measures and visualize the results. We have developed an R package called "fragility" to offer user-friendly functions for such purposes. This article provides an overview of methods for assessing and visualizing the fragility of individual studies as well as pairwise and network meta-analyses, introduces the usage of the "fragility" package, and illustrates the implementations with several worked examples.

摘要

随着人们对研究可重复性和可复制性的日益关注，对科学结果脆弱性（或稳健性）的评估越来越受到关注。脆弱指数被提出用于量化二分类结局的临床研究中统计显著性的稳健性。它定义为可以改变统计显著性的最小事件状态修改。它有助于临床医生评估结论的可靠性。许多因素可能会影响脆弱指数，包括修改事件状态的治疗组、用于检验治疗与结局之间关联的统计方法以及预先指定的显著性水平。除了评估单个研究的脆弱性外，脆弱指数最近还扩展到了多个治疗比较的常规成对荟萃分析和网络荟萃分析。对于临床医生来说，计算这些指标并可视化结果并不容易。我们开发了一个名为“fragility”的 R 包，为这些目的提供了用户友好的功能。本文概述了评估和可视化单个研究以及成对和网络荟萃分析脆弱性的方法，介绍了“fragility”包的用法，并通过几个示例说明了实现方法。

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