IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v69y2020i2p429-457.html
   My bibliography  Save this article

A flexible parametric modelling framework for survival analysis

Author

Listed:
  • Kevin Burke
  • M. C. Jones
  • Angela Noufaily

Abstract

We introduce a general, flexible, parametric survival modelling framework which encompasses key shapes of hazard functions (constant; increasing; decreasing; up then down; down then up) and various common survival distributions (log‐logistic; Burr type XII; Weibull; Gompertz) and includes defective distributions (cure models). This generality is achieved by using four distributional parameters: two scale‐type parameters—one of which relates to accelerated failure time (AFT) modelling; the other to proportional hazards (PH) modelling—and two shape parameters. Furthermore, we advocate ‘multiparameter regression’ whereby more than one distributional parameter depends on covariates—rather than the usual convention of having a single covariate‐dependent (scale) parameter. This general formulation unifies the most popular survival models, enabling us to consider the practical value of possible modelling choices. In particular, we suggest introducing covariates through just one or other of the two scale parameters (covering AFT and PH models), and through a ‘power’ shape parameter (covering more complex non‐AFT or non‐PH effects); the other shape parameter remains covariate independent and handles automatic selection of the baseline distribution. We explore inferential issues and compare with alternative models through various simulation studies, with particular focus on evidence concerning the need, or otherwise, to include both AFT and PH parameters. We illustrate the efficacy of our modelling framework by using data from lung cancer, melanoma and kidney function studies. Censoring is accommodated throughout.

Suggested Citation

  • Kevin Burke & M. C. Jones & Angela Noufaily, 2020. "A flexible parametric modelling framework for survival analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(2), pages 429-457, April.
    • Handle: RePEc:bla:jorssc:v:69:y:2020:i:2:p:429-457
    DOI: 10.1111/rssc.12398
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssc.12398
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssc.12398?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
    ---><---

    Citations

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


    Cited by:

    1. Jie Min & Yili Hong & Caleb B. King & William Q. Meeker, 2022. "Reliability analysis of artificial intelligence systems using recurrent events data from autonomous vehicles," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 987-1013, August.
    2. Chrys Caroni, 2022. "Regression Models for Lifetime Data: An Overview," Stats, MDPI, vol. 5(4), pages 1-11, December.
    3. Anaya-Izquierdo, Karim & Jones, M.C. & Davis, Alice, 2021. "A family of cumulative hazard functions and their frailty connections," Statistics & Probability Letters, Elsevier, vol. 172(C).
    4. Adam Braima S. Mastor & Abdulaziz S. Alghamdi & Oscar Ngesa & Joseph Mung’atu & Christophe Chesneau & Ahmed Z. Afify, 2023. "The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data," Mathematics, MDPI, vol. 11(2), pages 1-26, January.

    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:jorssc:v:69:y:2020:i:2:p:429-457. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: https://edirc.repec.org/data/rssssea.html .

    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.