Author(s)

Justin Ushize Rutikanga^1,2*, Aliou Diop³, Charline Uwilingiyimana^2,4

¹African Institute of Mathematical Sciences Rwandan Center, Kigali, Rwanda.
²College of Agriculture, Animal Sciences and Veterinary Medicine (CAVM), University of Rwanda, Musanze, Rwanda.
³LERSTAD, Gaston Berger University, Saint Louis, Senegal.
⁴Department of Statistics Applied to Economy, INES Ruhengeri Institute of Applied Sciences, Musanze, Rwanda.

This paper aims to explore the application of Extreme Value Theory (EVT) in estimating the conditional extreme quantile for time-to-event outcomes by examining the functional relationship between ambulatory blood pressure trajectories and clinical outcomes in stroke patients. The study utilizes EVT to analyze the functional connection between ambulatory blood pressure trajectories and clinical outcomes in a sample of 297 stroke patients. The 24-hour ambulatory blood pressure measurement curves for every 15 minutes are considered, acknowledging a censored rate of 40%. The findings reveal that the sample mean excess function exhibits a positive gradient above a specific threshold, confirming the heavy-tailed distribution of data in stroke patients with a positive extreme value index. Consequently, the estimated conditional extreme quantile indicates that stroke patients with higher blood pressure measurements face an elevated risk of recurrent stroke occurrence at an early stage. This research contributes to the understanding of the relationship between ambulatory blood pressure and recurrent stroke, providing valuable insights for clinical considerations and potential interventions in stroke management.

Censored Data, Conditional Extreme Quantile, Kernel Estimator, Weibull Tail Coefficient

Rutikanga, J. , Diop, A. and Uwilingiyimana, C. (2024) Using Extreme Value Theory Approaches to Estimate High Quantiles for Stroke Data. Open Journal of Statistics, 14, 150-162. doi: 10.4236/ojs.2024.141007.