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Because of the prevalence of missing data for transfusion, we performed multiple imputation by chained equations to account for missing data [ 23- 25].
The population with no available serum creatinine result had a high prevalence of missing data for both smoking status (2,760 of 24,738 [11.2%]) and hemoglobin A1c results (10,866 of 24,739 [43.9%]), suggesting that this population may not attend primary care services frequently.
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The amount of missing data for Beliefs and Dispositions, while not negligible, represents a reasonably small amount of missing data.
The proportion of missing data for each locus per population.
Analyses should indicate the amount of missing data for covariates.
The frequencies of missing data for all variables were registered.
The prevalence of missing data was 2.1% for BMI, 3.7% for education, 0.5% for civil status, and 0.7% for smoking during pregnancy.
The prevalence of missing data ranged from 0.7%to1.8%8% for other variables related to possible confounding factors.
The prevalence of missing data was generally low with 2.5% for BMI, 3.7% for education and 0.7% for smoking during pregnancy.
Given the low prevalence of missing data, we conducted complete case analysis.
We will assess the prevalence of missing data and examine robustness of intervention effects under various assumptions regarding plausible patterns of missing data in sensitivity analyses.
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