In case-control sampling, subjects are selected into the study based on the outcome of interest. It was established long ago that proportional hazards regression can be applied to case-control data. However, each of the various estimation techniques available assumes that failure times are independently censored, an assumption often violated in observational studies. This paper proposes and analyzes methods for Cox regression analysis of survival data obtained through case-control sampling, but subject to dependent censoring. The methods are based on weighted estimating equations, with separate inverse weights used to account for the case-control sampling and to correct for dependent censoring. The proposed estimators are shown to be consistent and asymptotically normal, and consistent estimators of the asymptotic covariance matrices are derived. The methods are illustrated through an analysis of pre-transplant mortality among end-stage liver disease patients obtained from a national organ failure registry.

Doug Schaubel is a Professor of Biostatistics at the University of Michigan. He holds a PhD in Biostatistics from the University of North Carolina at Chapel Hill. His methodological research interests focus on survival analysis and recurrent event data. His collaborative work is largely motivated by issues arising in organ failure and transplantation, settings which feature complex data structures and censoring patterns not amenable to standard techniques. Doug collaborates with members of the University of Michigan Kidney Epidemiology and Cost Center (KECC) and Arbor Research Collaborative for Health. Doug previously won the CJS Award in 2008 with co-author Qing Pan.

Hui Zhang received her PhD in 2011 from the Department of Biostatistics at the University of Michigan. Prior to her arrival at Michigan, Hui received a Master's in Statistics from the Colorado State University, and a Bachelor of Economics from the University of Science and Technology of China. Hui's dissertation developed methods for case-control and clustered case-cohort data. Her work as a research assistant included analyzing end-stage renal disease registry data from Canada and the United States. Since graduating from Michigan, Hui has worked at the U.S. Food and Drug Administration; she is currently a Statistical Reviewer, with a primary focus on oncology drugs.

John D. Kalbfleisch is Professor Emeritus of Biostatistics and Statistics at the University of Michigan and Distinguished Professor Emeritus at the University of Waterloo. Jack received his Ph.D. in statistics in 1969 from the University of Waterloo. Before going to Michigan in 2002, he was at the State University of New York at Buffalo (1970-73) and at the University of Waterloo (1973-2002) where he was Chair of the Department of Statistics and Actuarial Science (1984-1990) and the Dean of the Faculty of Mathematics (1990-1998). His wide interests include life history and survival analysis, likelihood methods of inference, mixture and mixed effects models, and medical applications, particularly in the area of renal disease and organ transplantation.

Xu Shu is a Ph.D candidate at the University of Michigan Department of Biostatistics, where she obtained a Master of Science (Biostatistics) in 2012. Xu's undergraduate work was completed at Beijing Normal University, where she majored in Statistics. Xu's dissertation develops novel semiparametric methods for comparing gap times (i.e., times between successive events). As part of her work as a research assistant, Xu has analyzed kidney, liver and multi-organ transplant data obtained from a national organ failure registry. Methods developed in her dissertation were motivated by this database. She has also worked as a research assistant at the Center for Statistical Genetics. Xu is scheduled to complete her dissertation during the summer of 2015.

The award-winning paper will be presented by Doug Schaubel in a special session at the 43rd Annual Meeting of the Statistical Society of Canada to be held in Halifax, Nova Scotia, June 14 to 17, 2015.