Stpm: an R Package for Stochastic Process Model

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2017 Feb 25

PMID 28231764

Citations 3

Authors

Ilya Y Zhbannikov

Konstantin Arbeev

Igor Akushevich

Eric Stallard

Anatoliy I Yashin

Affiliations

Soon will be listed here.

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.

Conclusion: 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).

Citing Articles

Interpretable machine learning for high-dimensional trajectories of aging health.

Farrell S, Mitnitski A, Rockwood K, Rutenberg A PLoS Comput Biol. 2022; 18(1):e1009746.

PMID: 35007286 PMC: 8782527. DOI: 10.1371/journal.pcbi.1009746.

"Physiological Dysregulation" as a Promising Measure of Robustness and Resilience in Studies of Aging and a New Indicator of Preclinical Disease.

Arbeev K, Ukraintseva S, Bagley O, Zhbannikov I, Cohen A, Kulminski A J Gerontol A Biol Sci Med Sci. 2018; 74(4):462-468.

PMID: 29939206 PMC: 6417443. DOI: 10.1093/gerona/gly136.

A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.

He L, Zhbannikov I, Arbeev K, Yashin A, Kulminski A Genet Epidemiol. 2017; 41(7):620-635.

PMID: 28636232 PMC: 5643257. DOI: 10.1002/gepi.22058.

References

Yashin A, Arbeev K, Arbeeva L, Wu D, Akushevich I, Kovtun M . How the effects of aging and stresses of life are integrated in mortality rates: insights for genetic studies of human health and longevity. Biogerontology. 2015; 17(1):89-107. PMC: 4724574. DOI: 10.1007/s10522-015-9594-8. View

Manton K, Stallard E, Singer B . Projecting the future size and health status of the U.S. elderly population. Int J Forecast. 1992; 8(3):433-58. DOI: 10.1016/0169-2070(92)90057-g. View

McEwen B, Wingfield J . The concept of allostasis in biology and biomedicine. Horm Behav. 2003; 43(1):2-15. DOI: 10.1016/s0018-506x(02)00024-7. View

Okumiya K, Matsubayashi K, Wada T, Fujisawa M, Osaki Y, Doi Y . A U-shaped association between home systolic blood pressure and four-year mortality in community-dwelling older men. J Am Geriatr Soc. 1999; 47(12):1415-21. DOI: 10.1111/j.1532-5415.1999.tb01559.x. View

Woodbury M . A random-walk model of human mortality and aging. Theor Popul Biol. 1977; 11(1):37-48. DOI: 10.1016/0040-5809(77)90005-3. View

Laird N, Ware J . Random-effects models for longitudinal data. Biometrics. 1982; 38(4):963-74. View

Yashin A, Arbeev K, Akushevich I, Kulminski A, Akushevich L, Ukraintseva S . Stochastic model for analysis of longitudinal data on aging and mortality. Math Biosci. 2007; 208(2):538-51. PMC: 2084381. DOI: 10.1016/j.mbs.2006.11.006. View

Yashin A, Manton K, Stallard E . The propagation of uncertainty in human mortality processes operating in stochastic environments. Theor Popul Biol. 1989; 35(2):119-41. DOI: 10.1016/0040-5809(89)90013-0. View

Proust-Lima C, Taylor J . Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach. Biostatistics. 2009; 10(3):535-49. PMC: 2697347. DOI: 10.1093/biostatistics/kxp009. View

10.

Yashin A, Arbeev K, Akushevich I, Kulminski A, Akushevich L, Ukraintseva S . Model of hidden heterogeneity in longitudinal data. Theor Popul Biol. 2007; 73(1):1-10. PMC: 2268646. DOI: 10.1016/j.tpb.2007.09.001. View

11.

Yashin A, Manton K, Vaupel J . Mortality and aging in a heterogeneous population: a stochastic process model with observed and unobserved variables. Theor Popul Biol. 1985; 27(2):154-75. DOI: 10.1016/0040-5809(85)90008-5. View

12.

Arbeev K, Ukraintseva S, Akushevich I, Kulminski A, Arbeeva L, Akushevich L . Age trajectories of physiological indices in relation to healthy life course. Mech Ageing Dev. 2011; 132(3):93-102. PMC: 3064744. DOI: 10.1016/j.mad.2011.01.001. View

13.

Gould A, Boye M, Crowther M, Ibrahim J, Quartey G, Micallef S . Responses to discussants of 'Joint modeling of survival and longitudinal non-survival data: current methods and issues. report of the DIA Bayesian joint modeling working group'. Stat Med. 2015; 34(14):2202-3. PMC: 4682363. DOI: 10.1002/sim.6502. View

14.

Arbeev K, Akushevich I, Kulminski A, Arbeeva L, Akushevich L, Ukraintseva S . Genetic model for longitudinal studies of aging, health, and longevity and its potential application to incomplete data. J Theor Biol. 2009; 258(1):103-11. PMC: 2691861. DOI: 10.1016/j.jtbi.2009.01.023. View

15.

Yashin A, Ukraintseva S, Arbeev K, Akushevich I, Arbeeva L, Kulminski A . Maintaining physiological state for exceptional survival: What is the normal level of blood glucose and does it change with age?. Mech Ageing Dev. 2009; 130(9):611-8. PMC: 2764411. DOI: 10.1016/j.mad.2009.07.004. View

16.

Arbeev K, Akushevich I, Kulminski A, Ukraintseva S, Yashin A . Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival. Front Public Health. 2014; 2:228. PMC: 4222133. DOI: 10.3389/fpubh.2014.00228. View

17.

Yashin A, Manton K, Stallard E . Dependent competing risks: a stochastic process model. J Math Biol. 1986; 24(2):119-40. DOI: 10.1007/BF00275995. View

18.

Boutitie F, Gueyffier F, Pocock S, Fagard R, Boissel J . J-shaped relationship between blood pressure and mortality in hypertensive patients: new insights from a meta-analysis of individual-patient data. Ann Intern Med. 2002; 136(6):438-48. DOI: 10.7326/0003-4819-136-6-200203190-00007. View

19.

Yashin A, Arbeev K, Akushevich I, Kulminski A, Ukraintseva S, Stallard E . The quadratic hazard model for analyzing longitudinal data on aging, health, and the life span. Phys Life Rev. 2012; 9(2):177-88. PMC: 3392540. DOI: 10.1016/j.plrev.2012.05.002. View

20.

Henderson R, Diggle P, Dobson A . Joint modelling of longitudinal measurements and event time data. Biostatistics. 2003; 1(4):465-80. DOI: 10.1093/biostatistics/1.4.465. View