Change-point problems arise in a variety of practical situations including engineering and health science applications. There are generally two approaches for these problems. One is the likelihood approach, and the other is the Bayesian approach, which leads to the well-known Bayes-type tests. The change-point problem in logistic models has not been investigated extensively.

The aim of this thesis is to provide a unified approach of Bayes-type and Score test to the problem of testing for constancy of parameters in logistic regression models, and explore the connection between Bayes-type tests and Score tests. Statistics are derived for changes at unknown times in the parameters of a logistic regression model. Asymptotic distribution theory for the tests is discussed. Simulations are carried out to compare the power with other options from the literature.