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In order to minimise the bias due to missing participant data across a number of trials, the primary meta-analysis was conducted using a complete case analysis, as proposed in the Cochrane Handbook [23] and by Akl et al. [24].
Separate multinomial logistic regression analyses (using a complete case approach) were conducted for each cohort.
The second stage repeated this analysis using a complete case sample for each outcome.
Results using the multiple imputation approach and results using a complete case approach were quantitatively similar.
Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping.
Third, the prevalence of each outcome was then fully adjusted for all demographic, socioeconomic and environmental factors using a complete case mixed effects linear and logit regression models to account for clustering at school level.
Similar(50)
We used a complete case analysis assuming missing completely at random.
We used a complete case analysis.
For these reason, we also used a complete case analysis approach.
We used a complete case analysis approach because only 110 subjects (3%) were missing data from one or more variables.
We used a complete case analysis approach as we had insufficient information on non-responders to impute missing data.
More suggestions(15)
using a complete randomize
using a complete instrument
using a complete system
using a complete factorial
using a complete timing
using a complete network
using a complete model
using a complete separation
using a hypothetical case
using a complete deletion
using a complete rosette
using a complete package
using a predefined case
using a complete simulator
using a complete carcinogenesis
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