中医临床队列研究中缺失数据的分析处理

Analysis and Processing of Missing Data in Clinical Cohort Study of Traditional Chinese Medicine

中医诊疗以个体化辨证论治和复杂干预为特点,接近真实世界研究的队列设计更能充分显示中医诊疗优势,但由于队列研究存在样本量大、研究周期长、开放度高、允许偏倚等问题,直接或间接破坏了数据完整性。传统以删失、均值替代为主的数据处理方法容易造成样本浪费,降低了统计效能,而以多重填补和最大似然思想为基础的数据缺失处理方法较多又难以选择,且方法间存在误用的问题。若能按照数据缺失判定、方法选用及拟合、拟合效果评价三个步骤,并根据缺失数据的属性仔细辨别方法间的区别和使用条件,构建中医临床队列研究的缺失数据拟合处理路径,将有效提高数据质量,使拟合后数据接近患者治疗的真实世界,更确切和真实地反映中医药临床疗效。

Traditional Chinese medicine( TCM) diagnosis and treatment is characterized by individualized differentiation of symptoms and signs as well as complex intervention. The cohort study close to the real world research can fully highlight the advantages of TCM diagnosis and treatment. However,data integrity is directly or indirectly damaged due to the large sample size,long research cycle,high openness and allowable bias of cohort research. Traditional data processing methods based on deletion and mean substitution are prone to waste samples and reduce statistical efficiency,while data missing processing methods based on multi-filling and maximum likelihood are more and difficult to choose,and there are problems of misuse among methods. If the missing data fitting processing path can be constructed according to the three steps of data missing determination,method selection,fitting and fitting effect evaluation,and according to the attributes of missing data carefully distinguishing the differences between methods and using conditions,the data quality will be effectively improved,and the data after fitting will be close to patient treatment,which can more accurately and truly reflects the clinical efficacy of Chinese medicine.