Logic regression is a generalized regression and classification method that is able to make Boolean combinations
as new predictive variables from the original binary variables. Logic regression was introduced for case control or
cohort study with independent observations. Although in various studies, correlated observations occur due to different
reasons, logic regression have not been studied in theory and application to analyze of correlated observations
and longitudinal data.
Due to the importance of identifying and considering the interactions between variables in longitudinal studies,
in this paper we propose Transition Logic Regression as an extension of Logic Regression to binary longitudinal
data. AIC of the models are used as score function of Annealing algorithm. In order to assess the performance of
the method, simulation study is done in various conditions of sample size, first order dependency and interaction
effect. According to results of simulation study, by increasing the sample size, percentage of identification of true
interactions and MSE of estimations get better. As an application, we assess interaction effect of some SNPs on
HDL level over time in TLGS study using our proposed model.