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If fewer than five data points were missing the data was also extrapolated.
When analyzing data, we excluded cases pairwise, i.e., we excluded the resident only if they were missing the data required for the specific analysis (Pallant 2007).
When greater than 4 items were missing, the data were not imputed.
If more than 20% of the values were missing, the data were deemed invalid, and values were substituted by LOCF.
When data for a single quarter was missing, the data was assumed to be the same as the previous quarter.
If a variable was missing the data extractors then reviewed the entire chart a second and third time.
Similar(51)
Whats missing from the data: Details.
The authors did compare the missing data with the existing data.
Furthermore, during data analysis we encountered the problem of missing data especially the biochemical data.
Missing data can introduce bias depending on the missing data mechanism and the adopted missing data approach.
This increases the chance for missing data or unreliable data.
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