Predicting Causal Effects of Therapeutic Interventions in Cardiac Cancer Patients: A Competing Risks

Music City Center 

This study addresses the importance of analyzing multiple time-to-event outcomes in competing risk
settings, particularly for patients with rare disease. When multiple risk factors are present for the event
of interest, competing risk models yield more accurate insights than traditional survival models, as
overlooking these risks can result in biased conclusions. Data from the NCI
SEER-18 database (2000-2018) was utilized, focusing on patients diagnosed with soft tissue cancers,
including cardiac sarcoma. The study computed cumulative incidence and mortality risks while applying
causal inference techniques to understand the impact of treatments on patient survival.
Despite the limited sample size, the study findings support the model-based prediction of
treatment effects on rare malignancies. Surgical intervention and radiotherapy were linked to reduced
cause-specific mortality, while chemotherapy and other therapies did not show significant associations
with overall survival, highlighting the need for effective therapeutic strategies in managing cardiac
cancer.

Cause-specific hazard

Causal inference

Machine learning

Cardiac cancer

SEER. 

Section on Statistics in Epidemiology