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stpm: an R package for stochastic process model.

Publication ,  Journal Article
Zhbannikov, IY; Arbeev, K; Akushevich, I; Stallard, E; Yashin, AI
Published in: BMC bioinformatics
February 2017

The Stochastic Process Model (SPM) represents a general framework for modeling the joint evolution of repeatedly measured variables and time-to-event outcomes observed in longitudinal studies, i.e., SPM relates the stochastic dynamics of variables (e.g., physiological or biological measures) with the probabilities of end points (e.g., death or system failure). SPM is applicable for analyses of longitudinal data in many research areas; however, there are no publicly available software tools that implement this methodology.We developed an R package stpm for the SPM-methodology. The package estimates several versions of SPM currently available in the literature including discrete- and continuous-time multidimensional models and a one-dimensional model with time-dependent parameters. Also, the package provides tools for simulation and projection of individual trajectories and hazard functions.In this paper, we present the first software implementation of the SPM-methodology by providing an R package stpm, which was verified through extensive simulation and validation studies. Future work includes further improvements of the model. Clinical and academic researchers will benefit from using the presented model and software. The R package stpm is available as open source software from the following links: https://cran.r-project.org/package=stpm (stable version) or https://github.com/izhbannikov/spm (developer version).

Duke Scholars

Published In

BMC bioinformatics

DOI

EISSN

1471-2105

ISSN

1471-2105

Publication Date

February 2017

Volume

18

Issue

1

Start / End Page

125

Related Subject Headings

  • User-Computer Interface
  • Stochastic Processes
  • Models, Theoretical
  • Kaplan-Meier Estimate
  • Internet
  • Humans
  • Heart Diseases
  • Blood Glucose
  • Bioinformatics
  • Age Factors
 

Citation

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ICMJE
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Zhbannikov, I. Y., Arbeev, K., Akushevich, I., Stallard, E., & Yashin, A. I. (2017). stpm: an R package for stochastic process model. BMC Bioinformatics, 18(1), 125. https://doi.org/10.1186/s12859-017-1538-7
Zhbannikov, Ilya Y., Konstantin Arbeev, Igor Akushevich, Eric Stallard, and Anatoliy I. Yashin. “stpm: an R package for stochastic process model.BMC Bioinformatics 18, no. 1 (February 2017): 125. https://doi.org/10.1186/s12859-017-1538-7.
Zhbannikov IY, Arbeev K, Akushevich I, Stallard E, Yashin AI. stpm: an R package for stochastic process model. BMC bioinformatics. 2017 Feb;18(1):125.
Zhbannikov, Ilya Y., et al. “stpm: an R package for stochastic process model.BMC Bioinformatics, vol. 18, no. 1, Feb. 2017, p. 125. Epmc, doi:10.1186/s12859-017-1538-7.
Zhbannikov IY, Arbeev K, Akushevich I, Stallard E, Yashin AI. stpm: an R package for stochastic process model. BMC bioinformatics. 2017 Feb;18(1):125.
Journal cover image

Published In

BMC bioinformatics

DOI

EISSN

1471-2105

ISSN

1471-2105

Publication Date

February 2017

Volume

18

Issue

1

Start / End Page

125

Related Subject Headings

  • User-Computer Interface
  • Stochastic Processes
  • Models, Theoretical
  • Kaplan-Meier Estimate
  • Internet
  • Humans
  • Heart Diseases
  • Blood Glucose
  • Bioinformatics
  • Age Factors