In a two-part model, logistic regression was used to explain the incurrence of drug expenditure at the first stage and a linear mixed model that considered the multilevel structure of data modeled the cost, conditional upon incurring any expense.
For multilevel data, the resulting model is called the multilevel sequential binary logit model (MBL).
Hierarchical models are a natural solution for analyzing such multilevel data in a single model, since they explicitly account for conditional independences present in the hierarchical structure (Lee, in press).
Accounting for the within individual variation through the use of a mixed-effects model or multilevel data analysis with a modification to the csSAM test statistic may help improve the statistical significance of the csSAM test statistic in longitudinal studies.
The data are analyzed by multilevel modelling techniques and the findings should elucidate the manner in which psychoeducation affects the daily lives of patients with this disorder.
Methods: Multilevel modelling of health survey data on 126 853 individuals from 33 metropolitan areas in the UK, Republic of Ireland, Sweden, Norway, Finland, Spain, Belgium and Germany compared general health, longstanding illness, acute sickness, psychological distress and obesity with the average for all areas, accounting for education and social class.
We used dyadic multilevel path modeling to analyze our data.
Quantitative phase: multilevel logistic regression modeling using merged data from the 2003 Regional Health Survey of Castile and Leon and the 2001 census data (Spain).
The nested (i.e., multilevel or hierarchical) structure of the data was modeled using a two-level multilevel model.
Guo, G. & Zhao, H. Multilevel modeling for binary data.
