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There is a problem of missing data because it is not possible to measure the impact on the same individuals, as at each moment in time, each individual is either under the intervention being evaluated or not and thus he or she can not be in both.
Further, these methods do not extend easily to many time points or to missing data because of computational burden and estimation instability [42].
The method cannot properly handle cases where different subsequent levels are not separated by missing data, because they require a very narrow window to follow the shift.
This does not seem to be an artifact due to missing data, because in the region where sequence information for Lc1, Lc2, and Lc3 overlap the uncorrected distance between Lc1/Lc2 and L. flos-jovis (the "normal" Lychnis species in this study) is 30.3%/34.6%, whereas between Lc3 and L. flos-jovis it is 3.9%.
We excluded missing data because they were less than 5%% for all variables.
** Variables with missing data because several participants did not answer the item.
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The ΔPCT D2 D3 value was calculated for only 147 patients because of missing data and because of the death of some patients within this period.
Eight cases were excluded because of missing data or because the demographic categories to which they belonged were too small to permit meaningful analyses.
For several variables, analyses were not possible within the whole study group because of missing data or because variables had not been investigated in all three surveys.
Data from nine participants were excluded because of missing data or because they were deemed, at the time of data collection, to be too impaired to focus on the research tasks.
9 The information on type of surgical treatment is also nearly complete, with only 5% of cases lacking a surgery code, either because of missing data or because the patient did not have surgery.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com