Multidimensional Stochastic Process Model and its applications to analysis of longitudinal data with genetic information

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Copyright 2016 ACM.Stochastic Process Model has many applications in analysis of longitudinal biodemographic data. In general, such data contain various physiological variables (sometimes known as covariates or physiological indices). Longitudinal data can also contain genetic information available for all or a part of participants. Taking advantage from both genetic and non-genetic information can provide future insights into a broad range of processes describing aging-related changes in the organism. In this work, we implemented a multi-dimensional Genetic Stochastic Process Model (GenSPM) in newly developed software tool, an R-package stpm (available from CRAN:, which allows researchers performing such kind of analysis.






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Zhbannikov, I, K Arbeev and A Yashin (2016). Multidimensional Stochastic Process Model and its applications to analysis of longitudinal data with genetic information. ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. pp. 467–468. 10.1145/2975167.2985634 Retrieved from

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Ilya Zhbannikov

Biostatistician III

Konstantin Arbeev

Associate Research Professor in the Social Science Research Institute

Konstantin G. Arbeev received the M.S. degree in Applied Mathematics from Moscow State University (branch in Ulyanovsk, Russia) in 1995 and the Ph.D. degree in Mathematics and Physics (specialization in Theoretical Foundations of Mathematical Modeling, Numerical Methods and Programming) from Ulyanovsk State University (Russia) in 1999. He was a post-doctoral fellow in Max Planck Institute for Demographic Research in Rostock (Germany) before moving to Duke University in 2004 to work as a Research Scientist and a Senior Research Scientist in the Department of Sociology and the Social Science Research Institute (SSRI).  He is currently an Associate Research Professor in SSRI. Dr. Arbeev's major research interests are related to three interconnected fields of biodemography, biostatistics and genetic epidemiology as pertains to research on aging. The focus of his research is on discovering genetic and non-genetic factors that can affect the process of aging and determine longevity and healthy lifespan. He is interested in both methodological advances in this research area as well as their practical applications to analyses of large-scale longitudinal studies with phenotypic, genetic and, recently, genomic information. Dr. Arbeev authored and co-authored more than 150 peer-reviewed publications in these areas.


Anatoli I. Yashin

Research Professor in the Social Science Research Institute

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