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PRIME-IPD 系列第 1 部分。PRIME-IPD 工具促进了为 IPD 荟萃分析检索的研究数据集的验证和标准化。

PRIME-IPD SERIES Part 1. The PRIME-IPD tool promoted verification and standardization of study datasets retrieved for IPD meta-analysis.

机构信息

Bruyère Research Institute, University of Ottawa, 85 Primrose Ave, Ottawa, Ontario, K1R 6M1, Canada; School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, Ontario, K1G 5Z3, Canada.

Bruyère Research Institute, University of Ottawa, 85 Primrose Ave, Ottawa, Ontario, K1R 6M1, Canada; School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, Ontario, K1G 5Z3, Canada.

出版信息

J Clin Epidemiol. 2021 Aug;136:227-234. doi: 10.1016/j.jclinepi.2021.05.007. Epub 2021 May 24.

Abstract

OBJECTIVES

We describe a systematic approach to preparing data in the conduct of Individual Participant Data (IPD) analysis.

STUDY DESIGN AND SETTING

A guidance paper proposing methods for preparing individual participant data for meta-analysis from multiple study sources, developed by consultation of relevant guidance and experts in IPD. We present an example of how these steps were applied in checking data for our own IPD meta analysis (IPD-MA).

RESULTS

We propose five steps of Processing, Replication, Imputation, Merging, and Evaluation to prepare individual participant data for meta-analysis (PRIME-IPD). Using our own IPD-MA as an exemplar, we found that this approach identified missing variables and potential inconsistencies in the data, facilitated the standardization of indicators across studies, confirmed that the correct data were received from investigators, and resulted in a single, verified dataset for IPD-MA.

CONCLUSION

The PRIME-IPD approach can assist researchers to systematically prepare, manage and conduct important quality checks on IPD from multiple studies for meta-analyses. Further testing of this framework in IPD-MA would be useful to refine these steps.

摘要

目的

我们描述了一种系统的方法来准备个体参与者数据(IPD)分析中的数据。

研究设计和设置

一份指导文件,提出了从多个研究来源为荟萃分析准备个体参与者数据的方法,通过咨询相关指导和 IPD 专家制定。我们展示了如何将这些步骤应用于我们自己的 IPD 荟萃分析(IPD-MA)中的数据检查。

结果

我们提出了五个步骤来为荟萃分析(PRIME-IPD)准备个体参与者数据:处理、复制、插补、合并和评估。使用我们自己的 IPD-MA 作为示例,我们发现这种方法确定了数据中的缺失变量和潜在不一致,促进了研究间指标的标准化,确认了从研究人员那里收到了正确的数据,并为 IPD-MA 生成了一个单一、经过验证的数据集。

结论

PRIME-IPD 方法可以帮助研究人员系统地准备、管理和对来自多个研究的 IPD 进行重要的质量检查,以进行荟萃分析。在 IPD-MA 中进一步测试这一框架将有助于完善这些步骤。

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