In medicine, the risk prediction model is often based on the estimated
probability of an event (e.g., disease incidence) derived from a
survival regression model. If a person is at risk of multiple mutually
exclusive events, i.e., competing events, the occurrence of one event
may prevent the occurrence of any other competing event from
happening, or may alter the probability of the occurrence of other
event. If competing events are independent to each other, standard
survival regression models can be employed in risk prediction.
However, when competing events are not independent to each other,
treating competing events as usual noninformative censoring in
standard survival analysis modeling may result in bias in risk
prediction. Several statistical methods have been developed to
estimate the probability of occurrence of an event when people are
subject to competing risks. Proper selection of the method of risk
prediction is crucial to producing accurate result.
The objectives of the course are to disseminate the concepts, methods,
and the recent statistical tools for risk prediction with data
involving competing risks, and to enrich a network of researchers who
use data science and analytic methods in medicine. At the end of the
course, attendees will understand why standard statistical regression
models are not appropriate for analyzing data of competing risks, and
will be able to identify two different types of competing risks in
practice, and understand the importance of diagnostics in risk
prediction modeling.
The COURSE WILL BE LED BY JOYCE CHANG, PHD and JONATHAN YABES, PHD,
Departments of Medicine and Biostatistics, University of PITTSBURGH
and held in the GRADUATE SCHOOL OF PUBLIC HEALTH AUDITORIUM (G23) on
SATURDAY, APRIL 21 FROM 9AM-4:30PM.
culture
model building
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22/04/2018 Last update