sschrod/BITES
BITES: Balanced Individual Treatment Effect for Survival data
This tool helps medical researchers and clinicians predict how individual patients will respond to a specific treatment over time, especially when some patient outcomes are not fully observed (right-censored data). You input patient data including demographics, clinical factors, and treatment assignment, and it outputs personalized treatment effect predictions, which are forecasts of survival probabilities under different treatment scenarios for each patient. It is designed for biostatisticians, epidemiologists, and clinical trial analysts.
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Use this if you need to understand the individual causal impact of a treatment on patient survival, accounting for confounding factors and incomplete outcome data.
Not ideal if your data does not involve survival outcomes or if you are not interested in comparing treatment effects.
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19
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3
Language
Python
License
BSD-2-Clause
Category
Last pushed
Jul 24, 2023
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