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用于建立可重复分析坚实基础的纵向研究的初步数据分析。

Initial data analysis for longitudinal studies to build a solid foundation for reproducible analysis.

机构信息

Department of Mathematics, Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Capodistria, Slovenia.

Institute for Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.

出版信息

PLoS One. 2024 May 29;19(5):e0295726. doi: 10.1371/journal.pone.0295726. eCollection 2024.

Abstract

Initial data analysis (IDA) is the part of the data pipeline that takes place between the end of data retrieval and the beginning of data analysis that addresses the research question. Systematic IDA and clear reporting of the IDA findings is an important step towards reproducible research. A general framework of IDA for observational studies includes data cleaning, data screening, and possible updates of pre-planned statistical analyses. Longitudinal studies, where participants are observed repeatedly over time, pose additional challenges, as they have special features that should be taken into account in the IDA steps before addressing the research question. We propose a systematic approach in longitudinal studies to examine data properties prior to conducting planned statistical analyses. In this paper we focus on the data screening element of IDA, assuming that the research aims are accompanied by an analysis plan, meta-data are well documented, and data cleaning has already been performed. IDA data screening comprises five types of explorations, covering the analysis of participation profiles over time, evaluation of missing data, presentation of univariate and multivariate descriptions, and the depiction of longitudinal aspects. Executing the IDA plan will result in an IDA report to inform data analysts about data properties and possible implications for the analysis plan-another element of the IDA framework. Our framework is illustrated focusing on hand grip strength outcome data from a data collection across several waves in a complex survey. We provide reproducible R code on a public repository, presenting a detailed data screening plan for the investigation of the average rate of age-associated decline of grip strength. With our checklist and reproducible R code we provide data analysts a framework to work with longitudinal data in an informed way, enhancing the reproducibility and validity of their work.

摘要

初步数据分析(IDA)是数据管道的一部分,它发生在数据检索结束和数据分析开始之间,旨在解决研究问题。系统的 IDA 和对 IDA 结果的清晰报告是可重复研究的重要步骤。观察性研究的 IDA 通用框架包括数据清理、数据筛选和可能更新预先计划的统计分析。纵向研究中,参与者随着时间的推移被反复观察,这带来了额外的挑战,因为它们具有特殊的特征,在解决研究问题之前,应该在 IDA 步骤中考虑这些特征。我们提出了一种系统的方法,用于在进行计划的统计分析之前检查纵向研究中的数据属性。在本文中,我们专注于 IDA 的数据筛选元素,假设研究目标伴随着分析计划、元数据有很好的记录,并且已经进行了数据清理。IDA 数据筛选包括五种类型的探索,涵盖了随时间的参与情况分析、缺失数据评估、单变量和多变量描述的呈现以及纵向方面的描述。执行 IDA 计划将生成 IDA 报告,告知数据分析师有关数据属性和对分析计划可能产生的影响的信息——这是 IDA 框架的另一个元素。我们的框架通过聚焦于复杂调查中多个波次的数据收集的手握力结果数据来说明。我们在公共存储库上提供了可重现的 R 代码,为研究手握力的年龄相关下降平均速率提供了详细的数据筛选计划。通过我们的清单和可重现的 R 代码,我们为数据分析师提供了一个框架,以便以知情的方式处理纵向数据,从而提高他们工作的可重复性和有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5597/11135704/e1db59e38ef7/pone.0295726.g001.jpg

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