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Missing values analyses revealed that persons with missing values on mental and/or physical function scales were significantly more likely to be lower educated, have fewer basic and luxury goods and have worse physical and mental function scores at the 2004 baseline.
Analysis was performed using data from all study patients (n=274) after multiple imputation (MI) of missing values, and revealed the following prognostic variables to be independent predictors of poor survival: age ⩾65 years, nonepithelioid histology, ECOG PS 2 3, weight loss, chest pain and a baseline platelet count >400 × 10 l−10
Sensitivity analyses performed using data for which missing values were not imputed revealed no meaningful differences in results compared with those reported here for the imputed data.
A sensitivity analysis investigated the characteristics of individuals who were excluded from the main analyses because they provided incomplete data revealed evidence of disproportionally missing values (Table S2).
All the other items revealed a portion of missing values between 1.9 and 6.3%.
Replacing missing values with the level indicating no problem would reveal mean values of 83.3 for the reference men, 77.5 for RT and 81.2 for RP patients.
An examination of the missing data did not reveal any systematic patterns, and the number of missing values was small in the preliminary 13 EDIS items.
This reveals that linear spline interpolation estimator for two or more sequentially missing values have smaller error than the linear regression estimator.
The findings revealed that estimating heterozygosity, inbreeding coefficients, and genetic differentiation were substantially biased when missing values were imputed.
Missing values are grey.
Missing values are also white.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com