IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v80y2014icp78-88.html
   My bibliography  Save this article

Markov transition model to dementia with death as a competing event

Author

Listed:
  • Wei, Shaoceng
  • Xu, Liou
  • Kryscio, Richard J.

Abstract

This study evaluates the effect of death as a competing event to the development of dementia in a longitudinal study of the cognitive status of elderly subjects. A multi-state Markov model with three transient states: intact cognition, mild cognitive impairment (M.C.I.) and global impairment (G.I.) and one absorbing state: dementia is used to model the cognitive panel data; transitions among states depend on four covariates age, education, prior state (intact cognition, or M.C.I., or G.I.) and the presence/absence of an apolipoprotein E-4 allele (APOE4). A Weibull model and a Cox proportional hazards (Cox PH) model are used to fit the survival from death based on age at entry and the APOE4 status. A shared random effect correlates this survival time with the transition model. Simulation studies determine the sensitivity of the maximum likelihood estimates to the violations of the Weibull and Cox PH model assumptions. Results are illustrated with an application to the Nun Study, a longitudinal cohort of 672 participants 75+ years of age at baseline and followed longitudinally with up to ten cognitive assessments per nun.

Suggested Citation

  • Wei, Shaoceng & Xu, Liou & Kryscio, Richard J., 2014. "Markov transition model to dementia with death as a competing event," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 78-88.
    • Handle: RePEc:eee:csdana:v:80:y:2014:i:c:p:78-88
    DOI: 10.1016/j.csda.2014.06.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947314001856
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2014.06.014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Paul S. Albert & Dean A. Follmann, 2003. "A Random Effects Transition Model For Longitudinal Binary Data With Informative Missingness," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(1), pages 100-111, February.
    2. Yu, Lei & Tyas, Suzanne L. & Snowdon, David A. & Kryscio, Richard J., 2009. "Effects of ignoring baseline on modeling transitions from intact cognition to dementia," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3334-3343, July.
    3. Jane Xu & Scott L. Zeger, 2001. "Joint analysis of longitudinal data comprising repeated measures and times to events," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 375-387.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lei Liu & Xuelin Huang & John O'Quigley, 2008. "Analysis of Longitudinal Data in the Presence of Informative Observational Times and a Dependent Terminal Event, with Application to Medical Cost Data," Biometrics, The International Biometric Society, vol. 64(3), pages 950-958, September.
    2. Ardo Van Den Hout & Fiona E. Matthews, 2010. "Estimating stroke‐free and total life expectancy in the presence of non‐ignorable missing values," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 331-349, April.
    3. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    4. Song Zhang & Peter Müller & Kim-Anh Do, 2010. "A Bayesian Semiparametric Survival Model with Longitudinal Markers," Biometrics, The International Biometric Society, vol. 66(2), pages 435-443, June.
    5. Pei Wang & Erin L. Abner & Changrui Liu & David W. Fardo & Frederick A. Schmitt & Gregory A. Jicha & Linda J. Van Eldik & Richard J. Kryscio, 2023. "Estimating random effects in a finite Markov chain with absorbing states: Application to cognitive data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(3), pages 304-321, August.
    6. Sarah J. Ratcliffe & Wensheng Guo & Thomas R. Ten Have, 2004. "Joint Modeling of Longitudinal and Survival Data via a Common Frailty," Biometrics, The International Biometric Society, vol. 60(4), pages 892-899, December.
    7. Erning Li & Naisyin Wang & Nae-Yuh Wang, 2007. "Joint Models for a Primary Endpoint and Multiple Longitudinal Covariate Processes," Biometrics, The International Biometric Society, vol. 63(4), pages 1068-1078, December.
    8. Philipson, Pete & Hickey, Graeme L. & Crowther, Michael J. & Kolamunnage-Dona, Ruwanthi, 2020. "Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    9. repec:jss:jstsof:35:i09 is not listed on IDEAS
    10. Yingye Zheng & Patrick J. Heagerty, 2005. "Partly Conditional Survival Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 61(2), pages 379-391, June.
    11. Xiao Song & Yijian Huang, 2004. "A Corrected Pseudo-score Approach for Additive Hazards Model With Longitudinal Covariates Measured With Error," UW Biostatistics Working Paper Series 1049, Berkeley Electronic Press.
    12. Lei Liu & Robert A. Wolfe & Xuelin Huang, 2004. "Shared Frailty Models for Recurrent Events and a Terminal Event," Biometrics, The International Biometric Society, vol. 60(3), pages 747-756, September.
    13. Jeremy M. G. Taylor & Yongseok Park & Donna P. Ankerst & Cecile Proust-Lima & Scott Williams & Larry Kestin & Kyoungwha Bae & Tom Pickles & Howard Sandler, 2013. "Real-Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models," Biometrics, The International Biometric Society, vol. 69(1), pages 206-213, March.
    14. Yueh-Yun Chi & Joseph G. Ibrahim, 2006. "Joint Models for Multivariate Longitudinal and Multivariate Survival Data," Biometrics, The International Biometric Society, vol. 62(2), pages 432-445, June.
    15. Xiao Song & Marie Davidian & Anastasios A. Tsiatis, 2002. "A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 58(4), pages 742-753, December.
    16. Xiao Song & C. Y. Wang, 2008. "Semiparametric Approaches for Joint Modeling of Longitudinal and Survival Data with Time-Varying Coefficients," Biometrics, The International Biometric Society, vol. 64(2), pages 557-566, June.
    17. Zangdong He & Wanzhu Tu & Sijian Wang & Haoda Fu & Zhangsheng Yu, 2015. "Simultaneous variable selection for joint models of longitudinal and survival outcomes," Biometrics, The International Biometric Society, vol. 71(1), pages 178-187, March.
    18. Sarah Brown & Pulak Ghosh & Karl Taylor, 2012. "The Existence and Persistence of Household Financial Hardship," Working Papers 2012022, The University of Sheffield, Department of Economics.
    19. Lin, Huazhen & Li, Yi & Tan, Ming T., 2013. "Estimating a unitary effect summary based on combined survival and quantitative outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 129-139.
    20. Rizopoulos, Dimitris, 2012. "Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 491-501.
    21. Qi Gong & Douglas E. Schaubel, 2013. "Partly Conditional Estimation of the Effect of a Time-Dependent Factor in the Presence of Dependent Censoring," Biometrics, The International Biometric Society, vol. 69(2), pages 338-347, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:80:y:2014:i:c:p:78-88. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.