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Multiple imputation analysis adjusting the imputed values downwards by a range of values will assess the sensitivity of the analysis to the missing at random assumption [ 61].
The multiple imputation analysis gave similar results.
Missing data will be handled via multiple imputation analysis.
The effect of missing data will be investigated using multiple imputation analysis.
Sensitivity to missing data was investigated using a multiple imputation analysis.
Results for PCBs did not change during the multiple imputation analysis.
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The results for complete-case and multiple-imputation analysis were very similar.
The results were consistent with the multiple-imputation analysis, although the parameters were estimated with greater precision with imputation (data not shown).
Rubin [ 8, 9] and Little and Rubin [ 10] provide an overview of statistical methods for multiple imputation and analysis of data with missing values.
Pain/discomfort was not significant but showed a prognostic value after the multiple- imputation analysis; this may be partially related to the high increase in variance due to missing QoL data.
First, we compared models using multiple imputation (the main analysis of our study) with the complete case analysis (models restricted to subjects with complete data in covariates and replacing OC values < LOD with LOD/2 value).
More suggestions(15)
multiple model analysis
multiple imputation method
multiple imputation particle
multiple resolution analysis
multiple imputation process
multiple correspondence analysis
multiple sequence analysis
multiple imputation technique
multiple correlation analysis
multiple regression analysis
multiple imputation m
multiple alignment analysis
multiple factor analysis
multiple imputation procedure
multiple scale analysis
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