The National Program on
Complex Data Structures


at the University of Toronto, Medical Science Building
August 14 -17, 2006 -- 9 AM to 5 PM

Director: Paul N Corey
Co-directors: Jamie Stafford and Wendy Lou
Peter Song

Missing data are pervasive in biomedical and health studies. Handling missing values in data analysis is very tricky, simply because both modelling and inference are effectively based on information that is partially unobserved. However, there are some useful tools developed by statisticians, which may be primarily categorized into two kinds: imputation approach and model-based approach. In this session, I plan to cover both approaches, but will concentrate more on the latter one that appears popular in dealing with missing data arising from clinical trials. In particular, I will discuss EM-algorithm as part of the model-based approach. If time permits, I plan to introduce the inverse probability weighted GEE approach, which is now widely used in the analysis of incomplete longitudinal data.

Instructor: Peter Song

Peter Song is an Associate Professor in the Biostatistics Research Unit, Department of Statistics and Actuarial Science, University of Waterloo. He received his PhD in Statistics from the University of British Columbia, 1996. Prior to the appointment at Waterloo, he was a visiting Associate Professor at Department of Biostatistics, University of Michigan School of Public Health (2002-2003), and Associate/Assistant Professor at Department of Mathematics and Statistics (1996-2004), York University.

Peter Song's research interests include bioinformatics, biostatistics, generalized linear models, missing data problems in clinical trials, time series analysis, and longitudinal data analysis. He is eager to work both applied and theoretical problems in modelling and inference, with strong motivation from real world data analysis. He has published over 30 statistical methodology papers in various statistical journals. For more details, visit his personal webpage:

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