Abstract
BackgroundThe 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.ResultsWe 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.ConclusionIn 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).
Highlights
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 with the probabilities of end points
One of possible approaches to integrate biological concepts and statistical models is based on the quadratic hazard models which were first introduced several decades ago [16,17,18]
In this paper we present the R package, stpm, the first publicly available set of utilities which implements the SPM methodology in three specific cases covering analyses most frequently used in practice and, constituting a general framework for studying and modeling survival traits depending on random trajectories of variables
Summary
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). One of possible approaches to integrate biological concepts and statistical models is based on the quadratic hazard models ( known alternatively as Stochastic Process Models, SPM) which were first introduced several decades ago [16,17,18]. Such models were recently modified [19, 20] to incorporate several conceptual mechanisms with clear biological interpretation (such as homeostatic regulation, allostatic adaptation, stress resistance, adaptive capacity and physiological norms) relevant in the context of research on aging. Incorporation of available knowledge about regularities of aging-related changes developing in the human body into the model structure allows for addressing fundamental problems of aging dealing with age-related declines
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
