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Item-level and patient-level missing data are reported; instances of missing data exceeding 10% during each 14-day period during baseline and treatment intervals were investigated for item-wise quality of completion.
We made several pre-defined assumptions that allowed us to reliably impute missing data in many instances.
Given the large size of the study cohort, we also examined the database for missing data and, in such instances, additional sensitivity analyses were performed whereby all patients with missing data were included in the analysis as a separate variable.
For achieving the better effectiveness, GkNN regards all the imputed instances (i.e., the missing data been imputed) as observed data, which with complete instances (instances without missing values) together to iteratively impute other missing data.
For instance, missing data for whether the cord was cut with a new or sterile blade ranged from 39% in Eastern India to 77% in Dhanusha (Nepal).
Pan et al. [11, 12] propose a sample-based approach which samples negative instances from missing data and adopts nonuniform weighting.
As we used official records there were few instances of missing data (<4%).
Thus, even with lower accuracies, SWDA is the preferable classification model in instances of missing data.
The sample size varied slightly across the analyses due to instances of missing data.
Also shown are missing data levels as in some instances, sizeable proportions of respondents did not provide requisite information.
Income and education were used as covariates in the space-time model to improve predictions in instances of missing data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com