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Title: Nearest neighbor imputation in longitudinal studies Authors:  Shahla Faisal - Ludwig Maximilians University Munich (Germany) [presenting]
Christian Heumann - Ludwig-Maximilians-University Munich (Germany)
Abstract: Longitudinal data often comes with missing values. These values cannot be ignored as it can result in loss of important information regarding samples. Therefore, imputation is a good strategy to overcome this problem. We present a single imputation method based on weighted nearest neighbors that uses the information from other variables to estimate the missing values. These neighbors use the information from within the sample whose response is measured at different time points and between samples. The simulation results show that the suggested imputation method provides better results with smaller imputation errors. Moreover the method performs in high dimensional data as good as in low dimensional data sets.