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Multivariable analysis ensured that potentially confounding factors were considered.
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Where data were missing for >10% observations for a variable included in multivariable analysis, a sensitivity analysis was run using the final model fitting a dummy category for missing data to ensure similar results were obtained using the full sample.
multivariable analysis of variance.
Design: Cross-sectional multivariable analysis.
Then, the multivariable analysis tools are employed.
Multivariable analysis best identifies independent risk factors.
Fig. 4 Multivariable analysis of risk factors for hospital mortality.
EDS was not associated with reported migraine in multivariable analysis.
PRBC and FFP were included in the multivariable analysis.
Univariate data comprise only patients eligible for multivariable analysis.
On multivariable analysis, influenza-like prodrome predicted fatal outcome.
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