Department of Biostatistics and Medical Informatics, Faculty of Medicine, Fırat University, Elazig, Turkey.
J Eval Clin Pract. 2024 Oct;30(7):1413-1421. doi: 10.1111/jep.14065. Epub 2024 Jun 21.
We aimed to demonstrate the use of quantitative bias analysis (QBA), which reveals the effects of systematic error, including confounding, misclassification and selection bias, on study results in epidemiological studies published in the period from 2010 to mid-23.
The articles identified through a keyword search using Pubmed and Scopus were included in the study. The articles obtained from this search were eliminated according to the exclusion criteria, and the articles in which QBA analysis was applied were included in the detailed evaluation.
It can be said that the application of QBA analysis has gradually increased over the 13-year period. Accordingly, the number of articles in which simple is used as a method in QBA analysis is 9 (9.89%), the number of articles in which the multidimensional approach is used is 10 (10.99%), the number of articles in which the probabilistic approach is used is 60 (65.93%) and the number of articles in which the method is not specified is 12 (13.19%). The number of articles with misclassification bias model is 44 (48.35%), the number of articles with uncontrolled confounder(s) bias model is 32 (35.16%), the number of articles with selection bias model is 7 (7.69%) and the number of articles using more than one bias model is 8 (8.79%). Of the 49 (53.85%) articles in which the bias parameter source was specified, 19 (38.78%) used internal validation, 26 (53.06%) used external validation and 4 (8.16%) used educated guess, data constraints and hypothetical data. Probabilistic approach was used as a bias method in 60 (65.93%) of the articles, and mostly beta (8 [13.33%)], normal (9 [15.00%]) and uniform (8 [13.33%]) distributions were selected.
The application of QBA is rare in the literature but is increasing over time. Future researchers should include detailed analyzes such as QBA analysis to obtain inferences with higher evidence value, taking into account systematic errors.
本研究旨在展示定量偏倚分析（QBA）的应用，该方法可揭示系统误差（包括混杂、分类错误和选择偏倚）对 2010 年至 23 年中期发表的流行病学研究结果的影响。
通过在 Pubmed 和 Scopus 上使用关键词搜索，纳入本研究。根据排除标准排除从该搜索中获得的文章，并纳入应用 QBA 分析的文章进行详细评估。
可以说，13 年来，QBA 分析的应用逐渐增加。相应地，9 篇（9.89%）文章中简单地将 QBA 分析作为一种方法使用，10 篇（10.99%）文章中多维方法使用，60 篇（65.93%）文章中概率方法使用，12 篇（13.19%）文章中未指定方法。有 44 篇（48.35%）文章使用分类错误偏倚模型，32 篇（35.16%）文章使用未控制混杂因素偏倚模型，7 篇（7.69%）文章使用选择偏倚模型，8 篇（8.79%）文章使用多个偏倚模型。在指定偏倚参数来源的 49 篇（53.85%）文章中，19 篇（38.78%）使用内部验证，26 篇（53.06%）使用外部验证，4 篇（8.16%）使用有根据的猜测、数据约束和假设数据。60 篇（65.93%）文章中使用概率方法作为偏倚方法，主要选择β（8 [13.33%]）、正态（9 [15.00%]）和均匀（8 [13.33%]）分布。
QBA 的应用在文献中很少见，但随着时间的推移呈上升趋势。未来的研究人员应该包括详细的分析，如 QBA 分析，以获得更高证据价值的推论，同时考虑系统误差。
