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Risk-standardisation was performed using hierarchical logistic regression models.
Factors influencing under-utilization of ANC were assessed using hierarchical logistic regression analysis.
Each cohort is modeled separately using hierarchical logistic regression to construct five standardized predicted-to-expected risk ratios, which are then pooled to create a single hospital metric.
The effects that the change in x-ray distributions may have on performance between the two subpopulations was modelled using hierarchical logistic regression.
Multivariate models using hierarchical logistic regression assessed the association of weight outcomes at end of study with each demographic, weight loss history, psychological, and behavioral factor.
When using hierarchical logistic regression to separate hospital size from the patient level factors, this made little difference to the study findings.
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We used hierarchical logistic regression to examine predictors of discomfort.
To determine the factors associated with medication oversupply, we used hierarchical logistic regression because the data structure we used fit the model.
We used hierarchical logistic regression models to investigate this hypothesis using cross-sectional, Demographic and Health Survey data from 10 sub-Saharan African countries.
The analyses used hierarchical logistic regression modeling to examine the effects of parental migration status, household characteristics and family processes across different measures of child well-being.
We used hierarchical logistic regression models to assess the effect of exposure to ePRISM on physicians' change in use of bleeding avoidance strategies and the incidence of post-procedural bleeding, corrected for concurrent trends among control hospitals.
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