影响中医临床随访研究数据质量的常见问题及对策

Common problems and countermeasures affecting the data quality of clinical follow-up study of traditional Chinese medicine

随访研究作为队列研究中重要的研究方法,被广泛应用于中医药临床研究中,在中医临床研究中具有重要地位。但由于研究方案设计不规范、对随访要素考虑不充分以及数据插补方法选择不当等原因,常导致随访研究出现原始数据获取不足、随访率计算不准确和缺失数据插补不当等问题。从明确随访研究特点是设计科学随访方案、获取完整随访数据的必要前提,全面掌握并恰当选择随访方式是控制失访率的有效手段,采用新随访率计算方法是准确评估随访完整性的重要保障,根据数据缺失类型选择合适的插补方式是获得可靠研究结论的关键环节等方面,探讨临床随访研究中常见数据处理问题的对策,分析中医药领域随访研究数据处理的关键点。随访研究作为临床研究中重要的组成部分,具有其他研究不可替代的优势,做好数据处理工作,提升数据质量,获得能够客观反映实际情况的研究数据,对于临床决策的制定和科研工作的开展均具有重要意义。

As an important research method in cohort study, follow-up study is widely used in clinical research of traditional Chinese medicine, and plays an important role in clinical research of traditional Chinese medicine. However, due to the non-standard design of the research scheme, insufficient consideration of the elements of follow-up and improper selection of data interpolation methods, it often leads to some problems in follow-up studies, such as insufficient acquisition of raw data, inaccurate calculation of follow-up rate, improper interpolation of missing data and so on. From the clear characteristics of the follow-up study is a necessary prerequisite for designing a scientific follow-up scheme and obtaining complete follow-up data. A comprehensive grasp and appropriate selection of follow-up methods is an effective means to control the rate of missing follow-up. The use of the new follow-up rate calculation method is an important guarantee for the accurate evaluation of follow-up integrity. Choosing the appropriate interpolation method according to the type of data deficiency is the key link to obtain reliable research conclusions and so on to explore the countermeasures of common data processing problems in clinical follow-up study. The key points of data processing in follow-up study in the field of traditional Chinese medicine were analyzed. As an important part of clinical research, follow-up study has irreplaceable advantages in other studies. It can do a good job in data processing, improve data quality, and obtain research data that can objectively reflect the actual situation. It is of great significance for the formulation of clinical decision-making and the development of scientific research.