ABSTRACT

Survival analysis is a class of statistical methods for analyzing data where the outcome variable is the time until the occurrence of an event of interest. It is extremely useful for studying many different kinds of events in medicine, economics, engineering, sociology, including death, the onset of disease, equipment failures, job terminations, retirements, marriages, etc. In most applications, the survival data are collected over a finite period of time. For example, in a cancer study, some patients may not have reached the endpoint of interest. Since having been introduced by Cox, the proportional hazard regression, also known as Cox regression model, is the default choice when dealing with time to-event data. In Bayesian survival analysis, the semiparametric proportional hazard model assigns a nonparametric prior to the baseline hazard function. The primary goal of this study is to examine the effectiveness of the new three-drug treatment regimen when compared to the standard two-drug regimen in improving survival among HIV-infected patients.