多水平logistic模型及其在流行病学调查数据中的应用

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广东药学院硕士研究生学位论文
多水平 logistic 模型及其在流行病学调查数据中的应用
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.
广东药学院 硕士学位论文 多水平logistic模型及其在流行病学调查数据中的应用 姓名：骆华萍 申请学位级别：硕士 专业：流行病学与卫生统计学 指导教师：张丕德
20100501
广东药学院硕士研究生学位论文
多水平 logistic 模型及其在流行病学调查数据中的应用
Hale Waihona Puke 中文摘要:目的：探讨多水平 logistic 模型的基本理论及其在流行病学调查数据上的应用， 旨在研究多水平 logistic 模型在实际应用过程中的方法学问题，为以后层次结构数 据的有效分析提供参考。
本研究的主要内容包括多水平 logistic 模型理论的基本原理与方法、实例拟合 过程（包括深圳市社区居民健康状况调查和广州市居民吸烟情况调查数据）、分析步 骤、方法比较及结果解释等。
本研究的数据预处理采用 SAS9.2 软件，多水平模型分析采用 MLwiN 和 SAS9.2 软 件，数据缺失值采用 SAS9.2 的 MI 过程进行处理。
忽略层次结构数据的组群效应，将会以损失资料信息的完整性为代价，使统计 结果失效，并有可能得出错误的结论。因此，在有层次结构的流行病学调查数据中， 多水平 logistic 模型是一个很好的选择，随着多水平 logistic 模型理论的完善和成熟， 多水平 logistic 模型在流行病学领域中将会有更大的优势和更广阔的应用前景。
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.
关键词 多水平 logistic 模型 层次结构数据 组内相关性 随机效应 固定效应 缺失数据
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广东药学院硕士研究生学位论文
多水平 logistic 模型及其在流行病学调查数据中的应用
Multilevel logistic model And its application of
epidemiological survey data
1
广东药学院硕士研究生学位论文
多水平 logistic 模型及其在流行病学调查数据中的应用
时研究个体变异和组间变异，能够分析固体效应和随机效应，对研究因素可做出准确 的估计和假设检验。但是，多水平模型也有一定的局限性，如多水平模型要求低级水 平和高级水平的残差方差服从正态分布或多元正态分布，参数估计较复杂，等等。另 外，有层次结构的数据不一定需要做多水平模型分析，首先要看其组内相关性的大小， 即是否存在组内聚集性，如果不存在数据聚集性，则用一般统计模型就可以了。在实 际应用中，要结合专业知识和数据特征来选择合适的统计方法。
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.
Master Candidate: Hua-ping Luo
Major: Epidemiology and health statistics
Supervior: Professor Pide Zhang
Abstract
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.