目的：模拟同一时间点数据完全缺失和部分缺失2种情况，通过填补值和实际值的对比，比较各填补方法对褪黑素（MT）时间序列的填补效果。方法同一时间点完全缺失时，比较实际值与 SPSS 5种填补方法填补结果；部分缺失时，除完全缺失的填补方法外，增加拟合时间序列模型填充。结果完全缺失时，临近点的中位数和线性插值法的填补结果和两因素析因设计资料方差分析结果更接近于实际。实际值波动幅度较小的时候，插值法拟合效果好；在实际值波动较大时，临近中位数拟合效果好。部分缺失时，拟合模型填充效果好。结论完全缺失时，如排除缺失值大幅波动，可以运用临近中位数和插值法对缺失值进行填充。在临近值波动幅度较小时，选用插值法填充值；在临近值波动幅度较大时，选用临近中位数填充值。部分缺失时，选用时间序列拟合模型填充。%Objective It is to simulate two cases of data completely missing and partially missing at the same time, and to compare the fill effect of melatonin (MT) time series of every method by comparing the fill value and the actual value.Meth-ods In the case of data completely missing at the same time, five SPSS fill methods were used to fill the simulated missing val-ues.Filling results were compared with the actual value.In the case of data partially missing at the same time, in addition to the completely missing filling methods, the time series model fitting was added to fill.Res ults In the case of data completely missing at the same time, median fill method of approaching points and linear interpolation results were closer to the actual val-ue.Two-factor factorial design analysis of variance results were better.When the actual value had small fluctuations, the line-ar interpolation result was closer to the actual value.When the actual value had small fluctuations, the linear interpolation re-sult was closer to the actual value.When the actual value had the larger fluctuations, the median fill method of approaching points result was closer to the actual value.In the case of data partially missing at the same time, the time series model fitting had the better result.Co nclusion In the case of data completely missing, excluding the very large increase and decrease, me-dian fill method of approaching points and linear interpolation can be used to fill missing values.When the actual value has small fluctuations, choose linear interpolation.When the actual value has the larger fluctuations, choose median fill method of approaching points.In the case of data partially missing, choose time series model to fill.
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