In clinical research missing data are common, and compared to demographics, clinical research produces generally smaller files, making a few missing data more of a problem than it is with demographic files. As an example, a 35 patient data file of 3 variables consists of 3 × 35 = 105 values if the data are complete. With only 5 values missing (1 value missing per patient) 5 patients will not have complete data, and are rather useless for the analysis. This is not 5 % but 15 % of this small study population of 35 patients. An analysis of the remaining 85 % patients is likely not to be powerful to demonstrate the effects we wished to assess. This illustrates the necessity of data imputation.
CITATION STYLE
Cleophas, T. J., & Zwinderman, A. H. (2016). Missing Data Imputation (35 Patients). In SPSS for Starters and 2nd Levelers (pp. 109–114). Springer International Publishing. https://doi.org/10.1007/978-3-319-20600-4_19
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