Analysis of clustered competing risks with application to a multicentre clinical trial

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dc.contributor.advisorGumedze, Freedom Nen_ZA
dc.contributor.authorFamilusi, Mary Ajibolaen_ZA
dc.date.accessioned2017-01-31T09:11:38Z
dc.date.available2017-01-31T09:11:38Z
dc.date.issued2016en_ZA
dc.description.abstractThe usefulness of time-to-event (survival) analysis has made it gain a wide applicability in statistically modelling research. The methodological developments of time-to-event analysis that have been widely adopted are: (i) The Kaplan-Meier method, for estimating the survival function; (ii) The log-rank test, for comparing the equality of two or more survival distributions; (m) The Cox proportional hazards model, for examining the covariate effects on the hazard function; and (iv) The accelerated failure time model, for examining the covariate effects on the survival function. Nonetheless, in time-to-event endpoints assessment, if subjects can fail from multiple mutually-exclusive causes, data are said to have competing risks. For competing risks data, the Fine and Gray proportional hazards model for sub-distributions has gained popularity due to its convenience in directly assessing the effect of covariates on the cumulative incidence function. Furthermore, sometimes competing risks data cannot be considered as independent because of a clustered design; for instance, in registry cohorts or multi-centre clinical trials. The Fine and Gray model has been extended to the analysis of clustered time-to-event data, by including random-centre effects or frailties in the sub-distribution hazard. This research focuses on the analysis of clustered competing risks with an application to the investigation of the management of pericarditis clinical trial (IMPI) dataset. IMPI is a multi- centre clinical trial that was carried out from 19 centres in 8 African countries with the principal objective of assessing the effectiveness and safety of adjunctive prednisolone and Mycobacterium indicus pranii immunotherapy, in reducing the composite outcome of death, constriction or cardiac tamponade, requiring pericardial drainage in patients with probable or definite tuberculous pericarditis. The clinical objective in this thesis is therefore to analyse time to these outcomes. In addition, the risk factors associated with these outcomes were determined, and the effect of the prednisolone and M. indcus pranii was examined, while adjusting for these risk factors and considering centres as a random effect. Using Cox proportional hazards model, it was found that age, weight, New York Heart Association (NYHA) class, hypotension, creatinine, and peripheral oedema show a statistically significant association with the composite outcome. Furthermore, weight, NYHA class, hypotension, creatinine and peripherial oedema show a statistically significant association with death. In addition, NYHA class and hypotension show a statistically significant association with cardiac tamponade. Lastly, prednisolone, gender, NYHA class, tachycardia, haemoglobin level, peripheral oedema, pulmonary infiltrate and HIV status show a statistically significant association with constriction. A value of 0.1 significance level was used to identify variables as significant in the univariate model using forward stepwise regression method. The random effect was found to be significant in the incidence of composite outcomes of death, cardiac tamponade and constriction, and in the individual outcome of constriction, but this only slightly changed the estimated effect of the covariates as compared to when the random effect was not considered. Accounting for death as a competing event to the outcomes of cardiac tamponade or constriction, does not affect the effect of the covariates on these outcomes. In addition, in the multivariate models that adjust for other risk factors, there was no significant difference in the primary outcome between patients who received prednisolone, and those who received placebo, or between those who received M. indicus pranii immunotherapy, and those who received placebo.en_ZA
dc.identifier.apacitationFamilusi, M. A. (2016). <i>Analysis of clustered competing risks with application to a multicentre clinical trial</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/23763en_ZA
dc.identifier.chicagocitationFamilusi, Mary Ajibola. <i>"Analysis of clustered competing risks with application to a multicentre clinical trial."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2016. http://hdl.handle.net/11427/23763en_ZA
dc.identifier.citationFamilusi, M. 2016. Analysis of clustered competing risks with application to a multicentre clinical trial. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Familusi, Mary Ajibola AB - The usefulness of time-to-event (survival) analysis has made it gain a wide applicability in statistically modelling research. The methodological developments of time-to-event analysis that have been widely adopted are: (i) The Kaplan-Meier method, for estimating the survival function; (ii) The log-rank test, for comparing the equality of two or more survival distributions; (m) The Cox proportional hazards model, for examining the covariate effects on the hazard function; and (iv) The accelerated failure time model, for examining the covariate effects on the survival function. Nonetheless, in time-to-event endpoints assessment, if subjects can fail from multiple mutually-exclusive causes, data are said to have competing risks. For competing risks data, the Fine and Gray proportional hazards model for sub-distributions has gained popularity due to its convenience in directly assessing the effect of covariates on the cumulative incidence function. Furthermore, sometimes competing risks data cannot be considered as independent because of a clustered design; for instance, in registry cohorts or multi-centre clinical trials. The Fine and Gray model has been extended to the analysis of clustered time-to-event data, by including random-centre effects or frailties in the sub-distribution hazard. This research focuses on the analysis of clustered competing risks with an application to the investigation of the management of pericarditis clinical trial (IMPI) dataset. IMPI is a multi- centre clinical trial that was carried out from 19 centres in 8 African countries with the principal objective of assessing the effectiveness and safety of adjunctive prednisolone and Mycobacterium indicus pranii immunotherapy, in reducing the composite outcome of death, constriction or cardiac tamponade, requiring pericardial drainage in patients with probable or definite tuberculous pericarditis. The clinical objective in this thesis is therefore to analyse time to these outcomes. In addition, the risk factors associated with these outcomes were determined, and the effect of the prednisolone and M. indcus pranii was examined, while adjusting for these risk factors and considering centres as a random effect. Using Cox proportional hazards model, it was found that age, weight, New York Heart Association (NYHA) class, hypotension, creatinine, and peripheral oedema show a statistically significant association with the composite outcome. Furthermore, weight, NYHA class, hypotension, creatinine and peripherial oedema show a statistically significant association with death. In addition, NYHA class and hypotension show a statistically significant association with cardiac tamponade. Lastly, prednisolone, gender, NYHA class, tachycardia, haemoglobin level, peripheral oedema, pulmonary infiltrate and HIV status show a statistically significant association with constriction. A value of 0.1 significance level was used to identify variables as significant in the univariate model using forward stepwise regression method. The random effect was found to be significant in the incidence of composite outcomes of death, cardiac tamponade and constriction, and in the individual outcome of constriction, but this only slightly changed the estimated effect of the covariates as compared to when the random effect was not considered. Accounting for death as a competing event to the outcomes of cardiac tamponade or constriction, does not affect the effect of the covariates on these outcomes. In addition, in the multivariate models that adjust for other risk factors, there was no significant difference in the primary outcome between patients who received prednisolone, and those who received placebo, or between those who received M. indicus pranii immunotherapy, and those who received placebo. DA - 2016 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2016 T1 - Analysis of clustered competing risks with application to a multicentre clinical trial TI - Analysis of clustered competing risks with application to a multicentre clinical trial UR - http://hdl.handle.net/11427/23763 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/23763
dc.identifier.vancouvercitationFamilusi MA. Analysis of clustered competing risks with application to a multicentre clinical trial. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2016 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/23763en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Statistical Sciencesen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMathematical Statisticsen_ZA
dc.titleAnalysis of clustered competing risks with application to a multicentre clinical trialen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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