作者投稿
专家审稿
编辑办公
邮件订阅
RSS
Approach to Handling Missing Data in Clinical Trials
-
摘要:
在临床试验中，因各种原因导致数据缺失是很常见的现象。缺失值的存在可造成原数据集信息损失，降低研究结果的稳健性和有效性。因此，如何正确处理缺失值是临床试验中必须重视且谨慎对待的问题。本文介绍了临床试验中缺失值的发生原因、类型及常见处理方法，旨在提高研究人员对缺失值的认识，减少缺失值处理方法的误用。需注意的是，临床试验中应对缺失值的最佳方法是严格预防和/或减少数据缺失的发生，而非事后处理。
Abstract:Missing data commonly occur in clinical trials due to various reasons. The occurrence of missing data will cause information loss of the original data， and reduce the robustness and validity of the research results. Therefore， how to adequately deal with missing data should be considered with caution in clinical trials. This article introduces the causes and types of missing data， as well as several common methodological approaches to addressing missing data， aiming to promote researchers’ understanding and improve the quality of handling missing data in trials. Nevertheless， the best way to deal with missing data is to prevent or reduce the occurrence of data being missing in clinical trials， rather than relying on post hoc statistical analyses.
-
Key words:
- clinical trials /
- missing data /
- deletion method /
- interpolation method
-
点击查看大图
计量
- 文章访问数: 178
- HTML全文浏览量: 14
- PDF下载量: 57
- 被引次数: 0
下载:
