Séance Séminaire

Séminaire de Probabilités et Statistique

Regression Models for Quantile Function Data Applied to CT-Scans of Asthmatic Patients

We are interested in evaluating the efficacy of Benralizumab, a medication to treat asthma, by using tomography scans captured during expiration and inspiration before and after one year of treatment. The medical working hypothesis posits that patients with improved conditions will exhibit enhanced expiration scans after treatment, which is manifested by higher Hounsfield unit values. This results in a shift to the right in the histogram built from post-treatment image compared to the pre-treatment one.

Irpino and Verde (2015) mimicked the classical linear regression method so that it can be applied to quantile functions instead of real-valued observations. We generalize their approach and obtain confidence intervals and laws of the estimators in the model via a maximum likelihood approach.

The model was implemented in a Python code and applied to a real data set of patients treated. Ongoing research aims to predict 2D-histograms post-treatment from CT scans during inspiration and expiration after registration, along with corresponding pre-treatment histograms, while including scalar covariates.

Joint work with Pierre Lafaye de Micheaux and Nicolas Molinari

Séminaire en salle 109, également retransmis sur zoom : https://umontpellier-fr.zoom.us/j/94087408185