Sentence examples for multilevel regression methods from inspiring English sources
Sample level variables were analyzed using multilevel regression methods to allow for the repeat measurements from each patient.
Multilevel regression methods enable researchers to explicitly include the hierarchical nature of practice data into their analyses [ 15].
The aim of this paper is to introduce multilevel regression methods as useful techniques for the analysis of patient data in practice-based disease management evaluation.
Applying multilevel regression methods is of particular relevance when patient outcomes are regarded as heterogeneous, as is typically the case with disease management.
Thus, to analyze the impact of the Dutch approach to disease management for type 2 diabetes, we conducted an uncontrolled, practice-based cohort study using multilevel regression methods.
This paper introduces multilevel regression methods as valuable techniques to evaluate 'real-world' disease management approaches in a manner that produces meaningful findings for everyday practice.
Stepwise multiple linear regression and multilevel linear regression methods were used to estimate the causal relationship between QOL change between baseline and 12 months and independent variables.
Marginal and multilevel logistic regression methods can estimate associations between hospital-level factors and patient-level 30-day mortality outcomes after cardiac surgery.
To allow for this data structure, and the binary nature of the outcome variable (choice of psychiatry or not), the analysis was performed using multilevel logistic regression methods.
As we work with hierarchical data whereby observations of students and their parents are nested within that of schools, we applied multilevel logistic regression methods.
We examined gender specific associations between the prevalence of poor body satisfaction and body mass index (BMI) with generalized additive models and applied multilevel logistic regression methods to estimate associations of body satisfaction with BMI, rural residence, parental education and income, and neighborhood household income.
