Abstract Number:
3557
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Amarise Little (1), Michael Wu (2)
Institutions:
(1) Fred Hutchinson Research Center, N/A, (2) Fred Hutchinson Cancer Center, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
Patients in anti-cancer clinical trials differentially experience toxicity to due to the treatment by race (Labriola & George, ASCO, 2021; Mizusawa, Cancer Med, 2023). We aim to better understand what the differences are in severity of toxicities (or adverse events) by estimating the difference network for severity of toxicities of Black race and white race samples during their time on immunotherapy clinical trials. The nature of the problem introduces several challenges including rare occurrences of adverse events, potential recurrence of adverse events, and adverse events' dependence on the type of treatments. We address these challenges by extending current graphical model frameworks to accommodate our specific data characteristics.
Keywords:
probit graphical model|adverse events| | | |
Sponsors:
Biometrics Section
Tracks:
Miscellaneous
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