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Joint modeling of longitudinal and time to event data with application to tuberculosis research

Due to tuberculosis (TB) being one of the top ten diseases in Africa with the
highest mortality rate, a crucial objective is to find the appropriate medication to
cure patients and prevent people from contracting the disease. Since this statistic
is not improving sufficiently, it is evident that there is a need for new anti-TB
drugs. One of the main challenges in developing new and effective drugs for the
treatment of TB is to identify the combinations of effective drugs when subsequent testing of patients in pivotal clinical trials are performed. During the early weeks of the treatment of TB, trials of the early bactericidal activity assess the decline in colony-forming unit (CFU) count of Mycobacterium TB in the sputum of patients containing smear-microscopy-positive pulmonary TB. A previously published dataset containing CFU counts of treated patients over 56 days is used to perform joint modeling of the nonlinear data over time and the patients’ sputum culture conversion (i.e., the time-to-event outcome). It is clear from the results obtained that there is an association between the longitudinal and time-to-event outcomes. / Mini Dissertation ( MSc (Advanced Data Analytics))--University of Pretoria, 2021. / South African Medical Research Council (SAMRC) / Statistics / MSc (Advanced Data Analytics) / Restricted

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/78510
Date January 2021
CreatorsNigrini, Sharday
ContributorsBurger, Divan A., snigrini1@gmail.com
PublisherUniversity of Pretoria
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeMini Dissertation
Rights© 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.

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