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.