Abstract:
Incorporating covariates into the future lifetime distribution is crucial to the
survival analysis. In this thesis a novel version of the exponential-Weibull distribution known as the extended exponential-Weibull (ExEW) distribution is developed and examined using the Lehmann alternative II (LAII) technique. The basic
mathematical properties of the new ExEW distribution are derived. The maximum likelihood estimation (MLE) technique is used to estimate the unknown
parameters of the ExEW distribution. The estimators' performance is further
assessed using Monte Carlo simulations. Two real-world data sets are utilized
to show the applicability of the new distribution. Moreover, a fully parametric
accelerated failure time (AFT) model with a exible, novel modi ed exponential
Weibull baseline distribution called the extended exponential Weibull accelerated
failure time (ExEW-AFT) model is developed. The model is presented using the
multi-parameter survival regression model, where more than one distributional
parameter is linked to the covariates. The model formulation, probabilistic functions, and some of its sub-models are derived. The parameters of the developed
model are estimated using the maximum likelihood approach. An extensive simulation study is used to assess the estimates' performance using di erent scenarios
based on the baseline hazard shape. The developed model is applied to a real-life
right-censored COVID-19 data set from Sudan to illustrate the practical applicability of the developed ExEW-AFT model. A mixture cure model with ExEW
distribution is presented to include the fraction of unsusceptible (cured) individuals in the study. The developed models are compared with existing mode
