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The multiple imputations indicated a potential underestimation based on self reported data.
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The results from multiple imputation indicated that, despite selective response, the imputed prevalence estimates did not significantly differ from the crude prevalence estimates of health problems, indicating that the selective response did not result in highly biased prevalence estimates.
The c statistic was above 0.70 for the complete case and all multiple imputation models, indicating good sensitivity and specificity.
However, analyses from published technical data 4 and our sensitivity analysis using multiple imputation both indicate that bias resulting from missing data is negligible.
Results from the multiple imputation analysis, however, indicated consistency in findings in the reduced (complete case) and pooled (imputed) datasets.
As occupation is a likely determinant of treatment assignment and outcome, we carried out multiple imputations for the variable indicating occupational group.
Multiple imputation of missing stage indicated that the effect of age in older patients may have been slightly underestimated.
Missing values were imputed using multiple imputations.
Missing data were imputed using multiple imputations.
Said examination indicated that they were not missing completely at random; therefore, five multiple imputations were conducted.
The risk estimates after multiple imputations were similar to those in analyses based on women with known BMI, indicating that selection bias was not a major limitation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com