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

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