Meike Adani
Survival analysis: an algorithmic approach to lifetime prediction.
Rel. Mauro Gasparini. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Matematica, 2022
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Abstract
Clinical trials often collect and assess data of survival (or time-to-event) with the objective of comparing different treatments or identifying risk factors that are linked to individuals risk rate of experiencing an event, that can be death, tumor progression or any other meaningful clinical outcome. When dealing with these types of survival data, the most popular method is the Cox proportional hazards regression model, used to explore the relationship between survival experience and characteristics of patients. The standard outcome of the Cox model is the hazard ratio, a relative measure that informs on the rank of patients' risk among others, but does not meaningfully inform on individual patients.
However, especially in the context of personalized medicine, it is of interest to identify an accurate model for lifetime prediction on an individual level
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