研究脊髓损伤数据中的缺失:数据归算的挑战和影响
Lucie Bourguignon1,2,3, Louis P Lukas4,5,6, James D Guest7
1Department of Health Sciences and Technology (D-HEST), ETH Zurich, Universitätstrasse 2, 8092, Zürich, Switzerland. lucie.bourguignon@hest.ethz.ch.
BMC medical research methodology
|January 6, 2024
概括
在脊髓损伤 (SCI) 研究中处理缺失的数据至关重要. 定制的归算策略,而不是一刀切的方法,对于准确和可重复的SCI研究结果至关重要.
科学领域:
- 医学研究 医学研究
- 数据科学是数据科学.
- 临床试验中的临床试验.
背景情况:
- 大规模的数据收集对于罕见的疾病,如脊髓损伤 (SCI) 是至关重要的.
- 临床数据集中缺少的数据对分析构成重大挑战.
- 目前处理缺失数据的方法包括数据删除或归算.
研究的目的:
- 调查不同缺失数据处理方法对SCI研究成果的影响.
- 提高人们对SCI注册表中缺少数据的影响的认识.
- 为未来在SCI和其他领域面临缺失数据的研究提供指导方针.
主要方法:
- 对Sygen临床试验数据的分析 (n=797).
- 基于变量类型和缺失模式的缺失数据影响的评估.
- 归算策略的比较:平均归算,最后的观察转移 (LOCF) 和多重归算.
主要成果:
- 平均值的归算可以显著扭曲预期的结果.
- 对于重复测量而言,最好选择LOCF,因为测量迟迟失踪 (>=伤害后6个月).
- 对于SCI数据集来说,通用的归算策略是不够的.
结论:
- 根据数据量身定制的归算策略是必要的,考虑到缺失模式.
- 对于随着时间的推移而平稳的重复测量,建议使用LOCF.
- 对数据处理缺失的透明报告对于研究解释,透明度和可重复性至关重要.
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