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Multiple imputation has found many applications in forestry and ecology over the past several years.
Multiple imputation has emerged as an appropriate and flexible way of handling missing data.
Moreover, multiple imputation has been shown to be robust to departures from normality assumptions.
(Multiple imputation has not been used here since the added complexity was not deemed necessary).
Multiple imputation has potential to improve the validity of medical research.
The performance of multiple imputation has been well studied and it has been shown to perform favourably.
Similar(45)
As missing values existed in our longitudinal cohort data, multiple imputation had to be performed before the GEE analysis and causal network construction.
Furthermore, although bias is a major concern, most studies that have used various methods to try to account for nonresponse (e.g., inverse probability weighting and multiple imputation) have not detected substantial bias in estimated measures of association [ 6- 10].
To examine recent use and reporting of multiple imputation, we searched four major general medical journals (New England Journal of Medicine, Lancet, BMJ, and JAMA) from 2002 to 2007 for articles reporting original research findings in which multiple imputation had been used.
Compared with the original map produced using indicator variables for missing data (5a), multiple imputation had only minor effects on the appearance of the 20 year latency analysis for all residences; as all six imputed maps (and their average) look virtually identical, we show only one.
Missing values can be dealt with by multiple imputation, which has been shown to cause less bias compared to complete case analysis, single imputation or the missing indicator method [ 48].
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CEO of Professional Science Editing for Scientists @ prosciediting.com