A Python package for survival analysis with competing risks
Project description
A Python package for survival analysis with competing risks, integrating neural networks and statistical inference. Provides tools for time-to-event prediction, model training with PyTorch backend, and comprehensive hypothesis testing.
Key Features: - Neural network models for time-varying and non-time-varying survival analysis - Prediction metrics including time-dependent AUC and C-index calculation - Bootstrap-based hypothesis testing (structure & significance tests) for model validation
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