IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v36y1987i3p309-317.html
   My bibliography  Save this article

Regression Analysis of Relative Survival Rates

Author

Listed:
  • T. Hakulinen
  • L. Tenkanen

Abstract

Survival from cancer or other chronic diseases is often measured using the relative survival rate. This, in turn, is defined as the ratio of the observed survival rate in the patient group under consideration to the expected survival rate in a group taken from the general population. At the beginning of the follow‐up period, apart from the disease under study, factors affecting survival (e.g. age and sex) should be similar in the two groups. This paper outlines how a proportional hazards regression model may be adapted to the relative survival rates using GLIM. The method is illustrated by data on lung cancer patients diagnosed in Finland in 1968–1970.

Suggested Citation

  • T. Hakulinen & L. Tenkanen, 1987. "Regression Analysis of Relative Survival Rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 309-317, November.
    • Handle: RePEc:bla:jorssc:v:36:y:1987:i:3:p:309-317
    DOI: 10.2307/2347789
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2347789
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2347789?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. Weihua Guan & Roberto G. Gutierrez, 2002. "Programmable GLM: Two user-defined links," Stata Journal, StataCorp LP, vol. 2(4), pages 378-390, November.
    2. Sanjib Basu & Ram C. Tiwari, 2010. "Breast cancer survival, competing risks and mixture cure model: a Bayesian analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 307-329, April.
    3. Lu Tang & Ling Zhou & Peter X. K. Song, 2019. "Fusion learning algorithm to combine partially heterogeneous Cox models," Computational Statistics, Springer, vol. 34(1), pages 395-414, March.
    4. Jun, Duk Bin & Kim, Kyunghoon & Park, Myoung Hwan, 2016. "Forecasting annual lung and bronchus cancer deaths using individual survival times," International Journal of Forecasting, Elsevier, vol. 32(1), pages 168-179.
    5. Øystein Kravdal, 1997. "The Attractiveness of an Additive Hazard Model: An Example from Medical Demography," European Journal of Population, Springer;European Association for Population Studies, vol. 13(1), pages 33-47, March.

    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:36:y:1987:i:3:p:309-317. 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.