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Quantile normalization was performed in R using normalizeBetweenArrays from the LIMMA library [55] and missing values imputed using LLS imputation (R: LLSimpute from the pcaMethod library with k = 20) [56].
Sensitivity analyses were conducted for worse eye and average eye by actual value and change from baseline in the ITT population with missing values imputed using multiple imputation methods, adjusting for baseline and without adjusting for investigator.
Furthermore, the main findings of this study were reproducible in the total cohort with missing values imputed using multivariate multiple imputation methods.
For inter-array comparability, data were quantile-normalized and missing values imputed with 10-nearest neighbors imputation.
Outcome variables did not have missing values imputed, but were included in the imputation models to predict missing values in other variables.
Incomplete data sets are frequently analyzed using multiple imputation, which involves creating multiple complete versions of the data with missing values imputed through random draws from distributions inferred from observed data (2).
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One YP did not complete a sufficient number of items to score the WEMWBS and four young people (1 %) had a missing value imputed.
Missing values were imputed with impute package [ 48].
Missing values were imputed using KNN-impute in the MultiExperiment Viewer [72], [73].
Missing values were imputed using the "impute" option from FastClus procedure (SAS).
Missing values were imputed in R using the method impute.knn in the library impute [ 14].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com