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By including only patients with non-missing data (complete case analysis) in our meta-analyses, our resultant estimates of effect could be biased if patients lost to follow-up were systematically different in ways that were related to our outcomes of interest (eg, if they were systematically more or less likely to prefer CPR).
In our multivariate statistical modelling, we created an indicator variable for missing data and restricted our analysis to non-missing data.
All percentages presented are calculated on non-missing data with the number available for analysis detailed in tables below.
To assure missing responses were not systematically biased by SOB, missing versus non-missing data for each phenotype scale was analyzed by SOB in 2×2 contingency tables.
The pattern of missing data and non-missing data is scattered throughout the observations.
Sensitivity analysis with restriction to non-missing data did not alter our interpretation of the results.
The analysis will be carried out using non-missing data.
Additionally, only 421 of mother-infant pairs (out of 551) have non-missing data for all risk factors reducing the power of the multivariate analysis.
Our analysis focussed on 13,955 patient observations with non-missing data for the variables chosen for this study.
Our analysis focused on 13,591 patient observations with non-missing data for the variables chosen for this study.
We included only women with non-missing data for a given stratification factor, including all noncases in each analysis by ER/PR status and menopausal status.
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