2013
DOI: 10.1093/ndt/gft221
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Multiple imputation: dealing with missing data

Abstract: In many fields, including the field of nephrology, missing data are unfortunately an unavoidable problem in clinical/epidemiological research. The most common methods for dealing with missing data are complete case analysis-excluding patients with missing data--mean substitution--replacing missing values of a variable with the average of known values for that variable-and last observation carried forward. However, these methods have severe drawbacks potentially resulting in biased estimates and/or standard err… Show more

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Cited by 107 publications
(90 citation statements)
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“…To keep the full sample size and thus the same number of comparisons, the mean value substitution method was used to replace the missing values with the average value calculated over all the values available from the same group. This method was described to be adequate when the missing occurred completely at random [29]. However, the mean substitution biases the standard error downwards due to a higher number of values centered on the mean.…”
Section: Methodsmentioning
confidence: 99%
“…To keep the full sample size and thus the same number of comparisons, the mean value substitution method was used to replace the missing values with the average value calculated over all the values available from the same group. This method was described to be adequate when the missing occurred completely at random [29]. However, the mean substitution biases the standard error downwards due to a higher number of values centered on the mean.…”
Section: Methodsmentioning
confidence: 99%
“…Because the exclusion of subjects with missing values could have resulted in biased prospective results, multiple imputation (fully conditional specification according to the Markov Chain Monte Carlo method) was used to obtain 5 imputed data sets (24,25). Rubin's rules were followed to obtain pooled estimates of the regression coefficients and their SEs across the imputed data sets (26).…”
Section: Statistical Analysesmentioning
confidence: 99%
“…As there were missing values on the additional candidate variables (table 1), we applied multiple imputation [19,20] before developing and validating the new models. Non-normally distributed variables were transformed to variables with a more normal distribution either logarithmically (cholesterol, phosphate, calcium and Kt/V) or by taking the square root (GFR and Karnofsky score) before imputing.…”
Section: Methodsmentioning
confidence: 99%