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In various areas of Nephrology research, we are in the presence of multiple competing events.
This renders the analysis quite simple and as a practical example makes it possible to visualize the flexibility with which DES models cope with multiple competing events.
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The analysis is complex due to competing events, clustering, multiple timescales and time-dependent period and intervention variables.
These outcomes are considered as competing events (risks).
Discharge alive from ICU was treated as competing events.
Determine competing events.
Finally, competing events impeded testing.
Here, the two endpoints represent competing events.
The microarray time-to-event data become more complicated when there are competing events, such as "progression" versus "death from non-cancer cause", i.e., the failure of a patient can occur due to one of multiple distinct causes.
However, in other clinical situations, more competing events can occur.
In this group, 243 (38.2% of 636) events of interest and 129 (20.3%) competing events were observed.
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