题目： Analysis of longitudinal data with missing data using R
报告人：Xiao-Hua Andrew Zhou
Professor, Department of Biostatistics, University of Washington, Seattle, WA
Director, Biostatistics Unit, U.S. Department of Veterans
Affair
Seattle
Medical
Center
时间：2006年7月18日上午9点
地点：数理学院309会议室
报告摘要：
Missing data problems are common in health research, sample surveys, and financial research. Longitudinal studies which collect data on a set of subjects repeatedly over time are subject to drop-out. For example, subjects drop out because they move, suffer side effects from drugs, or for other often unknown reasons. Similarly in sampling, survey “nonrespondents” are often ignored, although factors related to the objectives of the study such as income may influence the completeness of a subject’s response. Problems which involved missing data have historically been dealt with using a “complete-case analysis'' that ignores the missing data and therefore biases conclusions. In this talk I will discuss the method of multiple imputations for the analysis of missing data using R codes.
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Post By：2006/7/10 16:39:49
