Presenter: Lu Mao, Department of Biostatistics and Medical Informatics, University of Wisconsin - MadisonDate and Time: Wednesday, June 8, 2022, 12:00 p.m. – 2:00 p.m. ET (This course will be taught via Zoom)Sponsor: Lifetime Data Science Section
Registration Deadline: Wednesday, June 8, at 11:30 a.m. Eastern time
Description:This course provides an overview of the new statistical methodology for the analysis of composite time-to-event outcomes. These outcomes combine death and (possibly recurrent) nonfatal events, such as hospitalization, tumor progression, or infection, and are routinely used as the primary efficacy endpoint in modern phase-III clinical trials. The traditional approach to composite outcomes focuses on time to the first event, whichever type it is, using standard univariate survival analysis techniques. Recent years have seen a surge of more sophisticated and versatile methods, attracting the attention of both statisticians and practitioners. Examples of such methods include the win ratio (Pocock et al., 2012) and its various extensions, the restricted mean time in favor of treatment (a generalized restricted mean survival time), the event (or loss) rate ratio while alive, generalized semiparametric proportional odds regression models, and so on. They improve upon the traditional time-to-first-event analysis in (1) proper prioritization of death over nonfatal events; (2) fuller utilization of multiple/recurrent events; (3) clear and interpretable definition of effect-size estimands; and (4) flexible modeling of different outcome types. In the meantime, a number of user-friendly R-packages that implement the aforementioned methodology have become available. This short course will provide a survey of these methodological developments, along with some practical guidance on using the associated R-packages for real data analysis.Registration:Lifetime Data Science Section Members: $20ASA Members: $30Student ASA Member: $25Nonmembers: $45
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