IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v74y2018i3p977-985.html
   My bibliography  Save this article

A wild bootstrap approach for the Aalen–Johansen estimator

Author

Listed:
  • Tobias Bluhmki
  • Claudia Schmoor
  • Dennis Dobler
  • Markus Pauly
  • Juergen Finke
  • Martin Schumacher
  • Jan Beyersmann

Abstract

We suggest a wild bootstrap resampling technique for nonparametric inference on transition probabilities in a general time‐inhomogeneous Markov multistate model. We first approximate the limiting distribution of the Nelson–Aalen estimator by repeatedly generating standard normal wild bootstrap variates, while the data is kept fixed. Next, a transformation using a functional delta method argument is applied. The approach is conceptually easier than direct resampling for the transition probabilities. It is used to investigate a non‐standard time‐to‐event outcome, currently being alive without immunosuppressive treatment, with data from a recent study of prophylactic treatment in allogeneic transplanted leukemia patients. Due to non‐monotonic outcome probabilities in time, neither standard survival nor competing risks techniques apply, which highlights the need for the present methodology. Finite sample performance of time‐simultaneous confidence bands for the outcome probabilities is assessed in an extensive simulation study motivated by the clinical trial data. Example code is provided in the web‐based Supplementary Materials.

Suggested Citation

  • Tobias Bluhmki & Claudia Schmoor & Dennis Dobler & Markus Pauly & Juergen Finke & Martin Schumacher & Jan Beyersmann, 2018. "A wild bootstrap approach for the Aalen–Johansen estimator," Biometrics, The International Biometric Society, vol. 74(3), pages 977-985, September.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:3:p:977-985
    DOI: 10.1111/biom.12861
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.12861
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.12861?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
    ---><---

    References listed on IDEAS

    as
    1. Allignol, Arthur & Schumacher, Martin & Beyersmann, Jan, 2011. "Empirical Transition Matrix of Multi-State Models: The etm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i04).
    2. Davidson, Russell & Flachaire, Emmanuel, 2008. "The wild bootstrap, tamed at last," Journal of Econometrics, Elsevier, vol. 146(1), pages 162-169, September.
    3. D. Dobler & J. Beyersmann & M. Pauly, 2017. "Non-strange weird resampling for complex survival data," Biometrika, Biometrika Trust, vol. 104(3), pages 699-711.
    4. Jan Beyersmann & Susanna Di Termini & Markus Pauly, 2013. "Weak Convergence of the Wild Bootstrap for the Aalen–Johansen Estimator of the Cumulative Incidence Function of a Competing Risk," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 387-402, September.
    5. Thomas H. Scheike & Mei-Jie Zhang, 2003. "Extensions and Applications of the Cox-Aalen Survival Model," Biometrics, The International Biometric Society, vol. 59(4), pages 1036-1045, December.
    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. Nießl, Alexandra & Allignol, Arthur & Beyersmann, Jan & Mueller, Carina, 2023. "Statistical inference for state occupation and transition probabilities in non-Markov multi-state models subject to both random left-truncation and right-censoring," Econometrics and Statistics, Elsevier, vol. 25(C), pages 110-124.
    2. Ditzhaus, Marc & Pauly, Markus, 2019. "Wild bootstrap logrank tests with broader power functions for testing superiority," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 1-11.

    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. Tobias Bluhmki & Dennis Dobler & Jan Beyersmann & Markus Pauly, 2019. "The wild bootstrap for multivariate Nelson–Aalen estimators," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 97-127, January.
    2. Ditzhaus, Marc & Pauly, Markus, 2019. "Wild bootstrap logrank tests with broader power functions for testing superiority," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 1-11.
    3. Friedrich, Sarah & Pauly, Markus, 2018. "MATS: Inference for potentially singular and heteroscedastic MANOVA," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 166-179.
    4. D. Dobler & J. Beyersmann & M. Pauly, 2017. "Non-strange weird resampling for complex survival data," Biometrika, Biometrika Trust, vol. 104(3), pages 699-711.
    5. Friedrich, Sarah & Konietschke, Frank & Pauly, Markus, 2017. "A wild bootstrap approach for nonparametric repeated measurements," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 38-52.
    6. Umlauft, Maria & Placzek, Marius & Konietschke, Frank & Pauly, Markus, 2019. "Wild bootstrapping rank-based procedures: Multiple testing in nonparametric factorial repeated measures designs," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 176-192.
    7. Jan Beyersmann & Susanna Di Termini & Markus Pauly, 2013. "Weak Convergence of the Wild Bootstrap for the Aalen–Johansen Estimator of the Cumulative Incidence Function of a Competing Risk," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 387-402, September.
    8. 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.
    9. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    10. Daiki Maki, 2015. "Wild bootstrap tests for unit root in ESTAR models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 475-490, September.
    11. Dimitris Christopoulos & Miguel A. León-Ledesma, 2009. "On causal Relationships Between Exchange Rates and Fundamentals: Better Than You Think," Studies in Economics 0909, School of Economics, University of Kent.
    12. Congdon Fors, Heather & Houngbedji, Kenneth & Lindskog, Annika, 2019. "Land certification and schooling in rural Ethiopia," World Development, Elsevier, vol. 115(C), pages 190-208.
    13. Peter Jensen, 2010. "Testing the null of a low dimensional growth model," Empirical Economics, Springer, vol. 38(1), pages 193-215, February.
    14. Alessandro Palandri, 2020. "Size-Dependent Probability Bounds for t-Tests," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(3), pages 1-1.
    15. James G. MacKinnon & Matthew D. Webb & Morten Ø. Nielsen, 2017. "Bootstrap And Asymptotic Inference With Multiway Clustering," Working Paper 1386, Economics Department, Queen's University.
    16. Daiki Maki & Yasushi Ota, 2021. "Testing for Time-Varying Properties Under Misspecified Conditional Mean and Variance," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1167-1182, April.
    17. Emmanuel Flachaire, 2005. "More Efficient Tests Robust to Heteroskedasticity of Unknown Form," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 219-241.
    18. Timothy K Marcella & Scott M Gende & Daniel D Roby & Arthur Allignol, 2017. "Disturbance of a rare seabird by ship-based tourism in a marine protected area," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-23, May.
    19. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2023. "Fast and reliable jackknife and bootstrap methods for cluster‐robust inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 671-694, August.
    20. Cavaliere, Giuseppe & Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2011. "Testing For Unit Roots In The Presence Of A Possible Break In Trend And Nonstationary Volatility," Econometric Theory, Cambridge University Press, vol. 27(5), pages 957-991, October.

    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:bla:biomet:v:74:y:2018:i:3:p:977-985. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.