Competing risk in survival analysis

1:00 pm to 3:00 pm EST (10:00 am to 12:00 noon PST) | The session will be delivered live and online via the Gotowebinar system.

This webinar is part of the Advanced Methods Webinar Series

Survival analysis is interested in the study of the time until the occurrence of an event of interest (e.g., time to death). A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. For instance, in a study of cardiovascular death, death due to non-cardiovascular causes is a competing risk, as subjects who die of non-cardiovascular causes are no longer at risk of cardiovascular death. In this seminar we will discuss statistical methods for the analysis of survival data in the presence of competing risks. Examples and code will be used to illustrate the use of these methods.

View recorded presentation below.

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Dr. Peter AustinDr. Peter Austin is a Senior Scientist at ICES and a Professor in the Institute of Health Policy, Management and Evaluation at the University of Toronto. He has conducted research using large administrative health care databases for over twenty years. He conducts research in five different areas. First, the use of propensity score methods for estimating the effects of interventions using observational data. Second, in statistical methods for hospital profiling or hospital report cards. Third, in statistical methods for predicting patient outcomes. Fourth, analysis of survival data in the presence of competing risks. Fifth, methods for the analysis of multilevel or hierarchical data. He has published over 620 articles, including over 150 as first-author. A large number of these first-authored publications have been published in leading biostatistical journals, including Statistics in Medicine and Statistical Methods in Medical Research. For five successive years, he has been identified by Thomson Reuters or Clarivate Analytics as a highly-cited researcher ( due to the large number of papers that he has written that are amongst the top 1% of cited papers based on their year of publication and field of research.


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