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Modelling Growth Charts with Measurement Error: A Modern Perspective of Prediction of Abnormal Growth Responses in Young Children and Adults

Project Leader(s): 

Postdoctoral fellow: Dr. Taraneh Abarin, Public Health Sciences, University of Toronto

Lead faculty member: Dr. Laurent Briollais, Public Health Sciences, University of Toronto

Using modern statistical measurement error methodologies and analysis, we aim to efficiently and accurately discover and characterize predictive models of responses associated with abnormal growth development in young children and adults. This proposal is unique in scope and vision by addressing health issues that threaten the sustainability of the health care system.

Statistical Methods for Complex Survey Data

Project Leader(s): 

Dr. Changbao Wu, University of Waterloo

Project Website: 

http://www.stats.uwaterloo.ca/~mitacs/

Project team: 
Dr. Jiahua Chen, University of Waterloo
Dr. David Haziza, Université de Montréal
Dr. Jerry Lawless, University of Waterloo
Dr. Wilson Lu, Acadia University
Dr. Nancy Reid, University of Toronto
Dr. Jamie Stafford, University of Toronto
Dr. Brajendra Sutradhar, Memorial University of Newfoundland
Dr. Roland Thomas, Carleton University
Dr. Roland Thomas, Carleton University
Dr. Zilin Wang, Wilfrid Laurier University
Non-academic participants: 

Statistics Canada The Toronto Rehabilitation Institute Westat Inc.

Funding period: 
April 1, 2021 - March 31, 2021

The surveys being developed by government, health and social science organizations have increased in complexity and as a result, the data that is collected is similarly more complicated. Thus, this project focuses on developing new tools to address issues which arise during the analysis of this complex data including longitudinal data, information which is based on a set of repeated observations of an individual, or group of individuals, over time.

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