1
Log- linear modeling identified higher order associations among some of these same malformations.
2
Analysis: Parametric and nonparametric tests and general linear modeling were used as appropriate.
3
We used hierarchical linear modeling to control unbalanced data because of student attrition.
4
General linear modeling was used to compute means of self-reported oral health by treatment group.
5
Multivariable general linear modeling was used to identify factors associated with complications and prolonged LOS.
6
The study objective was addressed using generalized linear modeling.
7
Data were analyzed via hierarchical linear modeling to account for the nesting of offspring within litters.
8
Mixed linear modeling and logistic regression were performed.
9
Hierarchical linear modeling was used to examine the relationship between parent domains and youth progress in therapy.
10
Change in 4 accelerometer variables and the moderating role of psychosocial factors was tested using hierarchical linear modeling.
11
Hierarchical linear modeling was used to assess prevention effects and moderators such as baseline symptoms, race, and sex.
12
Generalized linear modeling using repeated measures was used to identify differences in outcome scores between fixation types over time.
13
Mixed-effects linear modeling with repeated measures was used to identify factors associated with depression and PTSD severity over time.
14
Generalized linear modeling was used to test associations between hematologic nadirs and dosimetric parameters, adjusting for body mass index.
15
The study used hierarchical linear modeling to examine the relations between individual and school characteristics, and students' psychosomatic symptoms.
16
Log- linear modeling was employed to take into account the dependence among all data sources and the heterogeneity of diabetic patients.
