Multilevel Logistic Model And Its Application Of Epidemiological Survey Data

In public health and epidemiology, large-scale surveys often follow a hierarchical structure of data as the surveys are based on multistage stratified cluster sampling.Examples of hierarchical data structures include persons nested within families,pupils nested within schools.Specific for hierarchical data sets is that observations are correlated.That is,the lower level belonging to the same higher level unit tend to be more alike than lower level units from different higher level units. At this circumstances,it's may not be suitable to using standard model such as logistic regression model. Standard approachs have the drawbacks of ignoring the potential importance of group-level attributes in influencing individual-level outcomes. In addition, if outcomes for individuals within groups are correlated, the assumption of independence of observations is violated, resulting in incorrect standard errors and inefficient estimates.The appropriate approach to analyzing such survey data is therefore based on nested sources of variability which come from different levels of hierarchy.Multilevel logsitic model differs from standard approaches,first:it allows the simultaneous examination of the effects of group-level and individuallevel predictors. Second:the nonindependence of observations within groups is accounted for, third:groups or contexts are not treated as unrelated, but are seen as coming from a larger population of groups, fourth: both interindividual and intergroup variation can be examined (as well as the contributions of individuallevel and group-level variables to these variations). Thus, multilevel analysis allows researchers to deal with the micro-level of individuals and the macro-level of groups or contexts simultanenously. In this study, we focus on the rationale for using multilevel logsitic model in public health research and epidemiology, summarizes the statistical methodology, and highlights some of the research questions that have been addressed using these methods. The advantages and disadvantages of multilevel logsitic model compared with standard methods are reviewed. The use of multilevel logsitic model raises theoretical and methodological issues related to the theoretical model being tested, the conceptual distinction between group- and individual-level variables, the ability to differentiate"independent"effects, the reciprocal relationships between factors at different levels, and the increased complexity that these models imply. The potentialities and limitations of multilevel logsitic model, within the broader context of understanding.We Use the Shenzhen Residents Health Survey and Guangzhou residents smoking survey multistage stratified cluster data.These study are designed to assist in all aspects of working with multilevel logistic regression models, including model conceptualization, model description, understanding of the structure of required multilevel data, estimation of the model via the statistical package SAS,MLwiN and interpretation of the results.It is found that failing to take into account the multilevel effects in the modeling, the standard logistic model has considerably either overestimated or underestimated ompared to the multilevel logistic model. Therefore, in the hierarchical struture data of epidemiological survey, the multilevel logistic model is a good choice.As the theory of multilevel logistic model of perfect and mature, multilevel logistic model will has greater advantages and more potential applications in the field of epidemiology.