Author(s)

Parvin Sarbakhsh¹, Yadollah Mehrabi^2*, Jeanine J. Houwing-Duistermaat³, Farid Zayeri⁴, Maryam Sadat Daneshpour⁵

¹Department of Statistics and Epidemiology, School of Public Health, Tabriz University of Medical Sciences, Tabriz, Iran.
²Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
³Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, Netherlands.
⁴Department of Biostatistics, School of Paramedicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
⁵Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

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.

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

Sarbakhsh, P. , Mehrabi, Y. , Houwing-Duistermaat, J. , Zayeri, F. and Daneshpour, M. (2016) Transition Logic Regression Method to Identify Interactions in Binary Longitudinal Data. Open Journal of Statistics, 6, 469-481. doi: 10.4236/ojs.2016.63042.