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Modeling data with informative cluster size

Modeling data with informative cluster size
具有信息丰富的簇大小的数据建模
批准号：
8736935
负责人：
Zhen Chen
金额：
$6.63万
依托单位：
EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
依托单位国家：
美国
项目类别：
财政年份：
--
资助国家：
美国
项目状态：
未结题
起止时间：
至
来源：
https://reporter.nih.gov/project-details/8736935
关键词：
DataEpidemiologic StudiesJointsLiteratureMethodsModelingOutcomeProceduresSamplingStatistical MethodsWeightdata modelingfollow-upnovel strategies

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英文摘要
When data are clustered due to longitudinal follow up or repeated sampling, special statistical methods need to be used to avoid bias and inefficiency. While in some clustered data the cluster size is pre-determined, in others it may be varying and correlated to the outcome of subunits, resulting in informative cluster size. When the cluster size is informative, standard statistical procedures that ignore cluster size may produce biased estimates. In this PI-initiated project, we will compare several methods that have been proposed in the literature to model data with informative cluster size, including within-cluster resampling, cluster weighted GEE, and joint modeling.
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