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has been cited by the following article:

• TITLE: Transition Logic Regression Method to Identify Interactions in Binary Longitudinal Data

  AUTHORS: Parvin Sarbakhsh, Yadollah Mehrabi, Jeanine J. Houwing-Duistermaat, Farid Zayeri, Maryam Sadat Daneshpour

  KEYWORDS: Logic Regression, Longitudinal Data, Transition Model, Interaction, TLGS Study, Low HDL, SNP

  JOURNAL NAME: Open Journal of Statistics, Vol.6 No.3, June 22, 2016

  ABSTRACT: Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which are associated with the response. In this study, we extended logic regression to longitudinal data with binary response and proposed “Transition Logic Regression Method” to find interactions related to response. In this method, interaction effects over time were found by Annealing Algorithm with AIC (Akaike Information Criterion) as the score function of the model. Also, first and second orders Markov dependence were allowed to capture the correlation among successive observations of the same individual in longitudinal binary response. Performance of the method was evaluated with simulation study in various conditions. Proposed method was used to find interactions of SNPs and other risk factors related to low HDL over time in data of 329 participants of longitudinal TLGS study.