%0 Journal Article
%T Using Extreme Value Theory Approaches to Estimate High Quantiles for Stroke Data
%A Justin Ushize Rutikanga
%A Aliou Diop
%A Charline Uwilingiyimana
%J Open Journal of Statistics
%P 150-162
%@ 2161-7198
%D 2024
%I Scientific Research Publishing
%R 10.4236/ojs.2024.141007
%X 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 trajec</span><span style=\"font-family:Verdana;\">tories and clinical outcomes in a sample of 297
stroke patients. The 24-ho</span><span style=\"font-family:Verdana;\">ur ambulatory blood pressure measurement
curves for every</span></span></span><span><span><span style=\"font-family:&quot;\"> </span></span></span><span><span><span style=\"font-family:&quot;\"><span style=\"font-family:Verdana;\">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 insi</span><span style=\"font-family:Verdana;\">ghts for clinical
considerations and potential interventions in stroke manag</span><span style=\"font-family:Verdana;\">ement.
%K Censored Data
%K Conditional Extreme Quantile
%K Kernel Estimator
%K Weibull Tail Coefficient
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=131567