Three levels Modeling and application in analysis of longitudinal blood pressure data

Multilevel (hierarchical) modeling is effective method for analyzing social data that set in more than one level. Multilevel Modeling is generalization of linear modeling in which regression coefficients are themselves given a model, whose parameters are also estimated from data. In this paper we want to illustrate about theoretical aspects and estimating method in three level modeling and application of this method in longitudinal Blood Pressure (BP) data.
Material and Data of this longitudinal study were extracted from annual observations of the male workers of Isfahan’s Mobarakeh Steel Company (IMSC), collected in the Health and Safety Executive office of the company between 2003 and 2009. In this research, we assessed the effect of Shift Work (SW) on Diastolic BP (DPB) with controlling BMI and age. In this paper, MLwiN and SPSS software were used to apply a Multilevel Modeling. This study consist of 6713 workers (45.2% regular day worker, 5.8% weekly rotating shift worker, and 49% routine rotating shift worker). In this study, after controlling confounding factor, SW not showed a significant association with DBP. High speed and high ability to fit models with high sample size are benefits of Iterative method rather likelihood method. And also suitable model of IMSC can be used to control the effect on BP on SW.