计算机科学
频数推理
荟萃分析
数据挖掘
考试(生物学)
统计假设检验
机器学习
统计
人工智能
贝叶斯概率
数学
医学
古生物学
内科学
生物
贝叶斯推理
作者
Victoria Nyawira Nyaga,Marc Arbyn
摘要
We developed metadta, a flexible, robust, and user-friendly statistical procedure that fuses established and innovative statistical methods for meta-analysis, meta-regression, and network meta-analysis of diagnostic test accuracy studies in Stata. Using data from published meta-analyses, we validate metadta by comparing and contrasting its features and output to popular procedures dedicated to the meta-analysis of diagnostic test accuracy studies; (midas [Stata], metandi [Stata], metaDTA [web application], mada [R], and MetaDAS [SAS]). We also demonstrate how to perform network meta-analysis with metadta, for which no alternative procedure is dedicated to network meta-analysis of diagnostic test accuracy data in the frequentist framework. metadta generated consistent estimates in simple and complex diagnostic test accuracy data sets. We expect its availability to stimulate better statistical practice in the evidence synthesis of diagnostic test accuracy studies.
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