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The multiple imputation included each UC-DAI component and WPAI UC Absenteeism, Presenteeism, and Overall work impairment domains as predictor variables.
Thus, our complete case analysis was performed on 792 women, and analysis using multivariate multiple imputation included 1268 women.
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As suggested by Carpenter and Kenward (2005), multiple imputation including health outcome was implemented in a Bayesian context.
If necessary, multiple imputation including demographic and other background variables as predictors will be used to allow inclusion of data from all participants and not simply those with data which would permits inclusion in mixed models.
We addressed this problem with different analytical strategies including complete cases only and multiple imputations including sensitivity analysis.
These scores were imputed by multiple imputation and included in analysis.
To validate our encoding strategy (ie, when an item was not recorded as abnormal, it was considered normal), we compared our results obtained for the association between neurological status at term and outcome at 2 years, with the results obtained with 50 datasets where non-recorded items were imputed using a multiple imputation module included in the SPSS Statistics software.
The multiple imputation analysis included all variables used in the final analysis with 10 imputations by using the Markov chain Monte Carlo method to produce imputed data.
The multiple imputation model included age, gender, total number of contacts, all 15 PHQ-15 questions and the outcome variable MUPS.
The multiple imputation models included all variables of interest for which complete data were available as well as indicator variables for each country to account for country-specific variation.
The multiple imputation model included 1) outcome variables collected at baseline, 6 months, and 12 months); 2) study site; 3) age at enrollment; 4) gender; 5) ethno-racial status and 5) Aboriginal status.
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