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No imputation was performed for missing data, therefore the denominator used in the analysis varied for each variable.
We recognize that this still represents a large proportion of missing data therefore the results should be interpreted with caution.
The Web-based survey did not permit missing data; therefore, all 200 subjects had complete survey data.
There was little missing data, therefore only households with complete information for all covariates for a given outcome were included in the analysis.
Four cases contained substantial amounts of missing data; therefore these were excluded from the analysis (one individual from the nutrition group and three from the combined group).
All variables had < 10% missing data; therefore, missing values were imputed with the median or mode for continuous or categorical variables, respectively.
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Our initial, more conservative power calculation had proposed: to recruit 480 participants; not to impute missing data statistically; therefore to allow for 25% loss to follow-up; and thus to yield 80% power to detect a slightly smaller effect size of 0.30 in CCQ scores.
The skewing of participation rates by student course grade demonstrates that the data are not missing completely at random and that missing data are therefore non-ignorable.
Mixed models are also unaffected by randomly missing data and therefore do not require imputation methods.
These restorations were regarded as missing data and, therefore, excluded from further analysis.
These models have the ability to handle missing data and therefore the intention-to-treat principle was adhered to [ 27].
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