IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v8y2012i1n23.html
   My bibliography  Save this article

Estimation and Asymptotic Theory for Transition Probabilities in Markov Renewal Multi-State Models

Author

Listed:
  • Spitoni Cristian

    (Leiden University Medical Centre)

  • Verduijn Marion

    (Leiden University Medical Centre)

  • Putter Hein

    (Leiden University Medical Centre)

Abstract

In this paper we discuss estimation of transition probabilities for semi–Markov multi–state models. Non–parametric and semi–parametric estimators of the transition probabilities for a large class of models (forward going models) are proposed. Large sample theory is derived using the functional delta method and the use of resampling is proposed to derive confidence bands for the transition probabilities. The last part of the paper concerns the presentation of the main ideas of the R implementation of the proposed estimators, and data from a renal replacement study are used to illustrate the behavior of the estimators proposed.

Suggested Citation

  • Spitoni Cristian & Verduijn Marion & Putter Hein, 2012. "Estimation and Asymptotic Theory for Transition Probabilities in Markov Renewal Multi-State Models," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-39, August.
  • Handle: RePEc:bpj:ijbist:v:8:y:2012:i:1:n:23
    DOI: 10.1515/1557-4679.1375
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/1557-4679.1375
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/1557-4679.1375?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. Jackson, Christopher, 2011. "Multi-State Models for Panel Data: The msm Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i08).
    2. Jinfeng Xu & John D. Kalbfleisch & Beechoo Tai, 2010. "Statistical Analysis of Illness–Death Processes and Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 66(3), pages 716-725, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jacobo de Uña-Álvarez & Luís Meira-Machado, 2015. "Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study," Biometrics, The International Biometric Society, vol. 71(2), pages 364-375, June.
    2. Jan Beyersmann & Hein Putter, 2014. "A note on computing average state occupation times," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(62), pages 1681-1696.
    3. Oliver Lunding Sandqvist, 2023. "A multistate approach to disability insurance reserving with information delays," Papers 2312.14324, arXiv.org.

    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. Qui Tran & Kelley M. Kidwell & Alex Tsodikov, 2018. "A joint model of cancer incidence, metastasis, and mortality," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 385-406, July.
    2. Qiu, Qinjing & Kawai, Reiichiro, 2022. "A decoupling principle for Markov-modulated chains," Statistics & Probability Letters, Elsevier, vol. 182(C).
    3. Jackson, Christopher, 2016. "flexsurv: A Platform for Parametric Survival Modeling in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i08).
    4. Vernon T. Farewell & Li Su & Christopher Jackson, 2019. "Partially hidden multi-state modelling of a prolonged disease state defined by a composite outcome," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 696-711, October.
    5. Gaffney, Edward & McCann, Fergal, 2019. "The cyclicality in SICR: mortgage modelling under IFRS 9," ESRB Working Paper Series 92, European Systemic Risk Board.
    6. Annalisa Orenti & Patrizia Boracchi & Giuseppe Marano & Elia Biganzoli & Federico Ambrogi, 2022. "A pseudo-values regression model for non-fatal event free survival in the presence of semi-competing risks," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 709-727, September.
    7. Biagini, Francesca & Groll, Andreas & Widenmann, Jan, 2013. "Intensity-based premium evaluation for unemployment insurance products," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 302-316.
    8. Fei Jiang & Sebastien Haneuse, 2017. "A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 112-129, March.
    9. Touraine, Célia & Gerds, Thomas A. & Joly, Pierre, 2017. "SmoothHazard: An R Package for Fitting Regression Models to Interval-Censored Observations of Illness-Death Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i07).
    10. Harrison T. Reeder & Junwei Lu & Sebastien Haneuse, 2023. "Penalized estimation of frailty‐based illness–death models for semi‐competing risks," Biometrics, The International Biometric Society, vol. 79(3), pages 1657-1669, September.
    11. Menggang Yu & Constantin T. Yiannoutsos, 2015. "Marginal and Conditional Distribution Estimation from Double-sampled Semi-competing Risks Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 87-103, March.
    12. Sharples, Linda D., 2018. "The role of statistics in the era of big data: Electronic health records for healthcare research," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 105-110.
    13. Alex Bottle & Chiara Maria Ventura & Kumar Dharmarajan & Paul Aylin & Francesca Ieva & Anna Maria Paganoni, 2018. "Regional variation in hospitalisation and mortality in heart failure: comparison of England and Lombardy using multistate modelling," Health Care Management Science, Springer, vol. 21(2), pages 292-304, June.
    14. Wildhaber, Mark L. & Albers, Janice L. & Green, Nicholas S. & Moran, Edward H., 2017. "A fully-stochasticized, age-structured population model for population viability analysis of fish: Lower Missouri River endangered pallid sturgeon example," Ecological Modelling, Elsevier, vol. 359(C), pages 434-448.
    15. Dongdong Li & X. Joan Hu & Mary L. McBride & John J. Spinelli, 2020. "Multiple event times in the presence of informative censoring: modeling and analysis by copulas," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 573-602, July.
    16. Yang Li & Hao Liu & Xiaoshen Wang & Wanzhu Tu, 2022. "Semi‐parametric time‐to‐event modelling of lengths of hospital stays," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1623-1647, November.
    17. Alexandra Grand & Regina Dittrich & Brian Francis, 2015. "Markov models of dependence in longitudinal paired comparisons: an application to course design," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(2), pages 237-257, April.
    18. Budhi Surya, 2021. "A new class of conditional Markov jump processes with regime switching and path dependence: properties and maximum likelihood estimation," Papers 2107.07026, arXiv.org.
    19. Linda Möstel & Marius Pfeuffer & Matthias Fischer, 2020. "Statistical inference for Markov chains with applications to credit risk," Computational Statistics, Springer, vol. 35(4), pages 1659-1684, December.
    20. Alejandra Marroig, 2023. "Transitions across states with and without difficulties in performing activities of daily living and death: a longitudinal comparison of ten European countries," European Journal of Ageing, Springer, vol. 20(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    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:bpj:ijbist:v:8:y:2012:i:1:n:23. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.