The Canadian Journal of Statistics Award is awarded annually by the SSC to the author(s) of an article published in the previous year in the journal, in recognition of the outstanding quality of the paper's methodological innovation and presentation.
Li Xing is an Assistant Professor in the Mathematics and Statistics Department at the University of Saskatchewan, Saskatoon, Canada. She obtained a Ph.D. in Statistics from the University of British Columbia (2014). Her research is focused on methodological development in statistical data science and software implementation.
Xuekui Zhang is a Canada Research Chair (Tier 2) in bioinformatics and biostatistics and an assistant professor of statistics at the University of Victoria. His research focuses on developing statistical methods and software for analyzing genomic data and other big data problems.
Igor Burstyn is an Associate Professor of Environmental and Occupational Health at the Drexel University, Philadelphia, USA. He earned a PhD in Environmental and Occupational Health from Utrecht University in the Netherlands (2001). His undergraduate training is in microbiology, and he holds a Master of Science degree in Occupational Hygiene (both from the University of British Columbia, Canada). Dr. Burstyn's contribution to methodological advancement in epidemiology was recognized by the award from the American College of Epidemiology for Outstanding Contribution to Epidemiology (2019)
Paul Gustafson is Professor and Head, Department of Statistics, University of British Columbia, Vancouver. His research interests include Bayesian methods, causal inference, evidence synthesis, measurement error, and partial identification.
“To Li Xing (University of Saskatchewan), Xuekui Zhang (University of Victoria), Igor Burstyn (Drexel University), and Paul Gustafson (University of British Columbia) for their paper entitled “On Logistic Box-Cox Regression for Flexibly Estimating the Shape and Strength of Exposure-Disease Relationships”, which appeared in The Canadian Journal of Statistics, Volume 49, Issue 3, 2021, Pages 808-825.”
This article deals with complicated issues, very important for epidemiological applications, surrounding inference and the shape of the relationship between a continuous exposure variable and a binary disease variable. A family of exposure-disease relationships indexed by a shape parameter based on the Box–Cox transformation is employed. The major benefits of using this model are clearly explained. This article attracted our attention due to its elegant combination of theoretical practical results and its obvious potential for epidemiological applications.