Extension of Logic regression to Longitudinal data: Transition Logic Regression

Authors

  • Daneshpour, Maryam Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences
  • Mehrabi, Yadollah Department of Epidemiology,School of Public Health, Shahid Beheshti University of Medical Sciences
  • Sarbakhsh, Parvin Department of Biostatistics, School of Paramedicine Shahid Beheshti University of Medical Sciences
  • Zayeri, Farid Department of Biostatistics, School of Paramedicine Shahid Beheshti University of Medical Sciences
Abstract:

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.

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Journal title

volume 19  issue 2

pages  63- 79

publication date 2015-02

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