IDEAS home Printed from https://ideas.repec.org/a/bla/istatr/v83y2015i3p493-510.html
   My bibliography  Save this article

A Conditional Approach to Measure Mortality Reductions Due to Cancer Screening

Author

Listed:
  • Zhihui (Amy) Liu
  • James A. Hanley
  • Olli Saarela
  • Nandini Dendukuri

Abstract

type="main" xml:id="insr12088-abs-0001"> The prevailing lack of consensus about the comparative harms and benefits of cancer screening stems, in part, from the inappropriate calculations of the expected mortality impact of a sustained screening programme. There is an inherent, and often substantial, time lag from the time of screening until the resulting mortality reductions begin, reach their maximum and ultimately end. However, the cumulative mortality reduction reported in a randomised screening trial is typically calculated over an arbitrarily defined follow-up period, including follow-up time where the mortality impact is yet to realise or where it has already been exhausted. Because of this, the cumulative reduction cannot be used for projecting the mortality impact expected from a sustained screening programme. For this purpose, we propose a new measure, the time-specific probability of being helped by screening, given that the cancer would have proven fatal otherwise. This can be decomposed into round-specific impacts, which in turn can be parametrised and estimated from the trial data. This represents a major shift in quantifying the benefits due to a sustained screening programme, based on statistical evidence extracted from existing trial data. We illustrate our approach using data from screening trials in lung and colorectal cancers.

Suggested Citation

  • Zhihui (Amy) Liu & James A. Hanley & Olli Saarela & Nandini Dendukuri, 2015. "A Conditional Approach to Measure Mortality Reductions Due to Cancer Screening," International Statistical Review, International Statistical Institute, vol. 83(3), pages 493-510, December.
  • Handle: RePEc:bla:istatr:v:83:y:2015:i:3:p:493-510
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/insr.12088
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Sandra J. Lee & Marvin Zelen, 2008. "Mortality Modeling of Early Detection Programs," Biometrics, The International Biometric Society, vol. 64(2), pages 386-395, June.
    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. Sudipta Saha & Zhihui Liu & Olli Saarela, 2021. "Instrumental variable estimation of early treatment effect in randomized screening trials," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 537-560, October.

    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. Elliot Lee & Mariel Lavieri & Michael Volk & Yongcai Xu, 2015. "Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity," Health Care Management Science, Springer, vol. 18(3), pages 363-375, September.
    2. Ester Vilaprinyo & Teresa Puig & Montserrat Rue, 2012. "Contribution of Early Detection and Adjuvant Treatments to Breast Cancer Mortality Reduction in Catalonia, Spain," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-8, January.
    3. Ester Vilaprinyo & Carles Forné & Misericordia Carles & Maria Sala & Roger Pla & Xavier Castells & Laia Domingo & Montserrat Rue & the Interval Cancer (INCA) Study Group, 2014. "Cost-Effectiveness and Harm-Benefit Analyses of Risk-Based Screening Strategies for Breast Cancer," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    4. Sandra J. Lee & Xiaoxue Li & Hui Huang & Marvin Zelen, 2018. "The Dana-Farber CISNET Model for Breast Cancer Screening Strategies: An Update," Medical Decision Making, , vol. 38(1_suppl), pages 44-53, April.

    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:istatr:v:83:y:2015:i:3:p:493-510. 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-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/isiiinl.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.