title: stpm: an R package for stochastic process model.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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).
authors.affiliation: Biodemography of Aging Research Unit (BARU) at Social Science Research Institute, Duke University, 2024 W. Main St., Durham, Box 90420, 27705, NC, USA. ilya.zhbannikov@duke.edu.
authors.forename: Ilya Y
authors.identifier: http://orcid.org/0000-0002-6502-6514
authors.initials: IY
authors.lastname: Zhbannikov
authors.affiliation: Biodemography of Aging Research Unit (BARU) at Social Science Research Institute, Duke University, 2024 W. Main St., Durham, Box 90420, 27705, NC, USA.
authors.forename: Konstantin
authors.initials: K
authors.lastname: Arbeev
authors.affiliation: Biodemography of Aging Research Unit (BARU) at Social Science Research Institute, Duke University, 2024 W. Main St., Durham, Box 90420, 27705, NC, USA.
authors.forename: Igor
authors.initials: I
authors.lastname: Akushevich
authors.affiliation: Biodemography of Aging Research Unit (BARU) at Social Science Research Institute, Duke University, 2024 W. Main St., Durham, Box 90420, 27705, NC, USA.|Duke Population Research Institute, Duke University, Durham, Box 90989, 27708-0989, NC, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Stallard
authors.affiliation: Biodemography of Aging Research Unit (BARU) at Social Science Research Institute, Duke University, 2024 W. Main St., Durham, Box 90420, 27705, NC, USA.|Duke Population Research Institute, Duke University, Durham, Box 90989, 27708-0989, NC, USA.
authors.forename: Anatoliy I
authors.initials: AI
authors.lastname: Yashin
mesh_terms: D000367:Age Factors; D001786:Blood Glucose / Q000032:analysis; D006331:Heart Diseases / Q000401:mortality / Q000473:pathology; D006801:Humans; D020407:Internet; D053208:Kaplan-Meier Estimate; D008962:Models, Theoretical; D013269:Stochastic Processes; D014584:User-Computer Interface
chemical_list: D001786:Blood Glucose
keywords: Life tables; Longitudinal data; Quadratic hazard; Risk factors; Stochastic process model