IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v55y2001i1p89-104.html
   My bibliography  Save this article

The Lognormal Distribution as a Model for Survival Time in Cancer, With an Emphasis on Prognostic Factors

Author

Listed:
  • P. Royston

Abstract

Despite their long history, parametric survival‐time models have largely been neglected in the modern biostatistical and medical literature in favour of the Cox proportional hazards model. Here, I present a case for the use of the lognormal distribution in the analysis of survival times of breast and ovarian cancer patients, specifically in modelling the effects of prognostic factors. The lognormal provides a completely specified probability distribution for the observations and a sensible estimate of the variation explained by the model, a quantity that is controversial for the Cox model. I show how imputation of censored observations under the model may be used to inspect the data using familiar graphical and other technques. Results from the Cox and lognormal models are compared and shown apparently to differ to some extent. However, it is hard to judge which model gives the more accurate estimates. It is concluded that provided the lognormal model fits the data adequately, it may be a useful approach to the analysis of censored survival data.

Suggested Citation

  • P. Royston, 2001. "The Lognormal Distribution as a Model for Survival Time in Cancer, With an Emphasis on Prognostic Factors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(1), pages 89-104, March.
    • Handle: RePEc:bla:stanee:v:55:y:2001:i:1:p:89-104
    DOI: 10.1111/1467-9574.00158
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9574.00158
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-9574.00158?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. Patrick Royston, 2007. "Multiple imputation of missing values: further update of ice, with an emphasis on interval censoring," Stata Journal, StataCorp LP, vol. 7(4), pages 445-464, December.
    2. Chrys Caroni, 2022. "Regression Models for Lifetime Data: An Overview," Stats, MDPI, vol. 5(4), pages 1-11, December.
    3. Shirin Moghaddam & John Newell & John Hinde, 2022. "A Bayesian Approach for Imputation of Censored Survival Data," Stats, MDPI, vol. 5(1), pages 1-19, January.
    4. Sun Eric & Jena Anupam B & Lakdawalla Darius & Reyes Carolina & Philipson Tomas J & Goldman Dana, 2010. "The Contributions of Improved Therapy and Earlier Detection to Cancer Survival Gains, 1988-2000," Forum for Health Economics & Policy, De Gruyter, vol. 13(2), pages 1-22, February.
    5. Nicholas Longford, 2008. "Inference with the lognormal distribution," Economics Working Papers 1104, Department of Economics and Business, Universitat Pompeu Fabra.
    6. Warisa Thangjai & Suparat Niwitpong, 2020. "Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation," Statistics in Transition New Series, Polish Statistical Association, vol. 21(5), pages 41-60, December.
    7. Selamawit Endale Gurmu, 2018. "Assessing Survival Time of Women with Cervical Cancer Using Various Parametric Frailty Models: A Case Study at Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia," Annals of Data Science, Springer, vol. 5(4), pages 513-527, December.
    8. Leon Chen, L. & Beck, Christian, 2008. "A superstatistical model of metastasis and cancer survival," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3162-3172.

    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:stanee:v:55:y:2001:i:1:p:89-104. 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: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    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.