A Pseudo-Value Approach to Causal Deep Learning of Semi-Competing Risks.

ABSTRACT: While mortality is often the main focus of cancer studies, non-fatal events, such as disease progression, can vitally impact patient outcomes. For example, recurrence after curative treatment is a crucial endpoint in lung cancer, affecting available second-line treatments and personalized care. Estimating the de-confounded effect of interventions on disease recurrence is a key aspect of assessing cancer treatments. However, semi-competing risks complicate causal inference when death prevents disease recurrence. Existing approaches for estimating causal quantities in semi-competing survival functions rely on complex objective functions with strong assumptions and are challenging to estimate accurately. To address these challenges, we propose a deep learning approach for estimating the causa

SUBMITTER: Salerno S 

PROVIDER: S-EPMC12629617 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature