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Real Data-Driven, Robust Survival Analysis on Patients with Parkinson's Disease

Abstract Number:

2786 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Iluppangama Iluppangama (1), Dilmi Abeywardana (1), Chris Tsokos (2)

Institutions:

(1) N/A, N/A, (2) Distinguished University Professor-USF, N/A

Co-Author(s):

Dilmi Abeywardana  
N/A
Chris Tsokos  
Distinguished University Professor-USF

First Author:

Malinda Iluppangama  
N/A

Presenting Author:

Malinda Iluppangama  
N/A

Abstract Text:

Parkinson's Disease (PD) is a devastating neurodegenerative disorder that affects millions of people around the globe. Many researchers are continuously working to understand PD and develop treatments to improve the condition of PD patients that affects their day-to-day lives. In the last decades, the treatment of Deep Brain Stimulation (DBS) has given promising results for motor symptoms by improving the quality of daily living of PD patients. In the methodology of the present study, we have utilized sophisticated statistical approaches such as Nonparametric, Semiparametric, and robust Parametric survival analysis to extract useful and important information about the long-term survival outcomes of the patients who underwent DBS for PD. Finally, we were able to conclude that the probabilistic behavior of the survival time of female patients is statistically different from that of male patients. Furthermore, we have identified that the probabilistic behavior of the survival times of Female patients is characterized by the 3-parameter Lognormal distribution while that of Male patients is characterized by the 3-parameter Weibull distribution.

Keywords:

Survival Analysis|COX-PH|Deep Brain Stimulation|Movement Disorder|Parkinson’s Disease|Parametric, Non-Parametric and Semi-Parametric Survival Analysis

Sponsors:

Biometrics Section

Tracks:

Survival Analysis

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I have read and understand that JSM participants must abide by the Participant Guidelines.

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