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For statistics we used a mixed effect model approach for ITT analysis with missing values [ 24].
We used a mixed effect logit model to investigate the relationship between patient mortality and nurse staffing (such as direct nursing care hours and nurse manpower) as well as other independent variables (such as hospital scale and severity of illness).
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We used a mixed effects model to parallel the original report based on the HDRS24 (Keller et al, 2000), dividing the observation periods into the first 4 weeks and then the subsequent 8 weeks of the 12-week trial.
We used a mixed effects modeling approach to examine change in systolic blood pressure over time.
We also used a mixed effects model with a random intercept to examine the impact of enalaprilat plus nitroglycerin versus enalaprilat alone on SBP.
Exploratory analysis of our immunological data used a mixed effects model.
We used a mixed effects model with longitudinally structured correlation, previously developed to analyse this dataset [ 6].
To test whether the largest dimension in the axial plane changed over time we used a mixed effects regression model.
We used a mixed effects logistic regression model, with protective ventilation as the dependent variable and the covariates used in the primary analysis as fixed effects.
To assess the effects of doxycycline treatment we used a mixed effects model to adjust for treatment and genotype and their interaction, with adjustment for correlation within mouse.
We used a mixed effects linear regression model in which the fixed effects were location (upwind, downwind, control) and year (2006, 2007, 2008), and used contrast statements to test specific interactions between them.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com