Olivier Bouaziz - A comprehensive framework for time to event predictions with right censored data

Séminaire « Probabilités et Statistique »

Predicting the time to an event of interest, based on patient attributes, is of great interest when analysing medical data. However, time to event data usually suffer from right-censoring which makes it difficult both to propose a prediction algorithm and to assess the quality of the predictions since the true times are not all observed. In this talk we will propose a comprehensive framework for evaluating the quality of predictions made by a prediction model. We present a new Mean Squared Error (MSE) criterion based on Inverse Probability Censoring Weighting (IPCW), a new method to construct prediction intervals based on the split conformal approach and a new statistical test to assess the importance of each variable. All those methods are designed to handle right-censoring. If time allows we will also discuss how to construct machine learning algorithms by combining pseudo-observations with the super learner, an ensemble method.

This talk is based on the following paper:


It is a joint work with Ariane Cwiling (MAP5) and Vittorio Perduca (MAP5).

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