Raymvp/HACSurv
HACSurv: A Hierarchical Copula-based Approach for Survival Analysis with Dependent Competing Risks
This project helps medical researchers or clinical trial statisticians perform survival analysis when multiple events (like different causes of death or complications) can happen simultaneously and influence each other. It takes patient outcome data, including various event types and censoring information, and generates predictions for when these events might occur, considering their dependencies. This allows for a more accurate understanding of patient prognosis and risk factors.
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Use this if you need to analyze time-to-event data where multiple, potentially dependent 'competing risks' can lead to the end of follow-up, and standard survival models don't adequately capture these interactions.
Not ideal if your survival analysis only involves a single event type or if you are confident that multiple event types are entirely independent of each other.
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Mar 05, 2025
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