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Title: Applied statistical methods for prediction modelling of upper limb functional recovery after stroke
Author: Al-Shallawi, Ahmad Najim Sheet
ISNI:       0000 0004 7969 9327
Awarding Body: Keele University
Current Institution: Keele University
Date of Award: 2019
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Stroke is the third largest cause of death in the world, with a significant contribution to disability. Motor function impairment, encompassing upper limb impairment, is the most significant post-stroke impairment. Such an impairment contributes to reducing a person's ability to complete daily activities, thus affecting their quality of life. Effective interventions, specifically targeted at upper limb recovery, are important, just as much as predictions of patient's post-stroke. Predictions have become essential in making accurate clinical decisions in stroke management, including selection of appropriate rehabilitation programs, referring into appropriate services, setting realistic goals by therapists and clinicians and predicting the level of dependence following discharge from the hospital. This research focuses on the prediction of upper limb recovery and function. Despite the current and widely used traditional statistical methods of prediction, the research here presents a developed modern method which focuses on prediction models of regression methods. This is because traditional methods have been shown to lack clinical usefulness and do not have meaningful acceptance in clinical practice. The modern method developed and adopted aims to give more beneficial and valid results from the prediction model.
Supervisor: Pandyan, A. ; Blana, D. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: R Medicine (General)