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Regression Modeling for Time-to-Event Data with Unknown Control-Group Event Times

Time-to-event data analysis without a well-defined time origin commonly occurs in observational studies that retrospectively collect survival endpoints. For instance, after enrolling participants who have or have not received a specific treatment, an event status can be observed for all participants; however, the start date of treatment is only identifiable for the treatment group. The corresponding time origin does not exist for the control group, resulting in missing survival time data. To address this challenge, we propose to adopt the proportional hazards model by regarding these missing time origins as nuisance parameters. Simulation studies and real-data analysis illustrate the applicability of this approach.

Abstract

Dr. Ding-Geng Chen (aka, Din Chen) is an elected fellow of the American Statistical Association, an elected fellow of the Royal Society of South Africa, an elected member of the Academy of Science of South Africa. He is now the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University, and also an extraordinary professor and the SARChI research chair in biostatistics at the University of Pretoria, and an honorary professor at the University of KwaZulu-Natal, South Africa. Dr. Chen was the Wallace H. Kuralt Distinguished professor in Biostatistics at the University of North Carolina at Chapel Hill, a professor in biostatistics at the University of Rochester Medical School, and the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics at Georgia South-ern University. He is a senior biostatistics consultant for biopharmaceuticals and government agencies with extensive expertise in biostatistics, clinical trials, and public health statistics. He has more than 300 referred professional publications, co-authored 12 books and co-edited 31 books on biostatistics, clinical trial methodology, meta-analysis, data science, causal inference, and public health statistics research and applications.