Bayesian approach to survival modeling of remission duration for acute leukemia

Abstract

The problem of analyzing time to event data arises in a number of applied fields like biology and medicine. This study constructs a survival model of remission duration from a clinical trial data using Bayesian approach. Two covariates; drug and remission status, were used to describe the variation in the remission duration using the Weibull proportional hazards model which forms the likelihood function of the regression vector. Using a uniform prior, the summary of the posterior distribution; Weibull regression model of four parameters ( η, µ,β1, β2, was obtained. With Laplace transform, initial estimates of the location and spread of the posterior density of the parameters were obtained. In this present study, data from children with acute leukemia was used. The information from the Laplace transform was used to find a density for the Metropolis random walk algorithm from Markov Chain Monte Carlos simulation to indicate the acceptance rate (24.55%).