How authors select covariates in the multivariate analysis of cancer studies in 10 oncology journals in Korea: a descriptive study

Science Editing Pub Date : 2024-02-20 DOI:10.6087/kcse.327
Mi Ah Han, Hae-Ran Kim, Sang Eun Yoon, Sun Mi Park, Boyoung Kim, Seo-Hee Kim, So-Yeong Kim
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Abstract

Purpose: Cancer is the leading cause of death in Korea, leading many investigators to focus on cancer research. We present the current practice of variable selection methods for multivariate analyses in cancer studies recently published in major oncology journals in Korea.Methods: We included observational studies investigating associations between exposures and outcomes using multivariate analysis from 10 major oncology journals published in 2021 in KoreaMed, a Korean electronic database. Two reviewers independently and in duplicate performed the reference screening and data extraction. For each study included in this review, we collected important aspects of the variable selection methods in multivariate models, including the study characteristics, analytic methods, and covariate selection methods. The descriptive statistics of the data are presented.Results: In total, 107 studies were included. None used prespecified covariate selection methods, and half of the studies did not provide enough information to classify covariate selection methods. Among the studies reporting selection methods, almost all studies only used data-driven methods, despite having study questions related to causality. The most commonly used method for variable selection was significance in the univariate model, with the outcome as the dependent variable.Conclusion: Half of the included studies did not provide sufficient information to assess the variable selection method, and most used a limited data-driven method. We believe that the reporting of covariate selection methods requires improvement, and our results can be used to educate researchers, editors, and reviewers to increase the transparency and adequacy of covariate selection for multivariable analyses in observational studies.
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在对韩国 10 种肿瘤学期刊上的癌症研究进行多变量分析时,作者如何选择协变量:一项描述性研究
目的:在韩国,癌症是导致死亡的主要原因,因此许多研究人员都把重点放在了癌症研究上。我们介绍了最近在韩国主要肿瘤学期刊上发表的癌症研究中进行多变量分析的变量选择方法:我们将韩国电子数据库 KoreaMed 中 2021 年发表的 10 种主要肿瘤学期刊中使用多变量分析调查暴露与结果之间关系的观察性研究纳入其中。两名审稿人分别独立进行参考文献筛选和数据提取。对于纳入本综述的每项研究,我们都收集了多变量模型中变量选择方法的重要方面,包括研究特点、分析方法和协变量选择方法。结果显示了数据的描述性统计:结果:共纳入 107 项研究。结果:共纳入 107 项研究,其中没有一项研究使用了预先指定的协变量选择方法,半数研究没有提供足够的信息来对协变量选择方法进行分类。在报告选择方法的研究中,尽管研究问题与因果关系有关,但几乎所有研究都只使用了数据驱动方法。最常用的变量选择方法是以结果为因变量的单变量模型中的显著性:结论:半数纳入的研究没有提供足够的信息来评估变量选择方法,大多数研究使用了有限的数据驱动方法。我们认为,协变量选择方法的报告需要改进,我们的结果可用于教育研究人员、编辑和审稿人,以提高观察性研究多变量分析中协变量选择的透明度和充分性。
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