IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v25y2005i2p168-177.html
   My bibliography  Save this article

A New Logistic Regression Approach for the Evaluation of Diagnostic Test Results

Author

Listed:
  • A. Cecile J. W. Janssens

    (Center for Clinical Decision Sciences, Department of Public Health, Erasmus MC, the Netherlands, a.janssens@erasmusmc.nl)

  • Yazhong Deng

    (Center for Clinical Decision Sciences, Department of Public Health, Erasmus MC, the Netherlands)

  • Gerard J. J. M. Borsboom

    (Center for Clinical Decision Sciences, Department of Public Health, Erasmus MC, the Netherlands)

  • Marinus J. C. Eijkemans

    (Center for Clinical Decision Sciences, Department of Public Health, Erasmus MC, the Netherlands)

  • J. Dik. F. Habbema

    (Center for Clinical Decision Sciences, Department of Public Health, Erasmus MC, the Netherlands)

  • Ewout W. Steyerberg

    (Center for Clinical Decision Sciences, Department of Public Health, Erasmus MC, the Netherlands)

Abstract

The value of a dichotomous diagnostic test is often described in terms of sensitivity, specificity, and likelihood ratios (LRs). Although it is known that these test characteristics vary between subgroups of patients, they are generally interpreted, on average, without considering information on patient characteristics, such as clinical signs and symptoms, or on previous test results. This article presents a reformulation of the logistic regression model that allows to calculate the LRs of diagnostic test results conditional on these covariates. The proposed method starts with estimating logistic regression models for the prior and posterior odds of disease. The regression model for the prior odds is based on patient characteristics, whereas the regression model for the posterior odds also includes the diagnostic test of interest. Following the Bayes theorem, the authors demontsrate that the regression model for the LR can be derived from taking the differences between the regression coefficients of the 2 models. In a clinical example, they demonstrate that the LRs of positive and negative test results and the sensitivity and specificity of the diagnostic test varied considerably between patients with different risk profiles, even when a constant odds ratio was assumed. The proposed logistic regression approach proves an efficient method to determine the performance of tests at the level of the individual patient risk profile and to examine the effect of patient characteristics on diagnostic test characteristics.

Suggested Citation

  • A. Cecile J. W. Janssens & Yazhong Deng & Gerard J. J. M. Borsboom & Marinus J. C. Eijkemans & J. Dik. F. Habbema & Ewout W. Steyerberg, 2005. "A New Logistic Regression Approach for the Evaluation of Diagnostic Test Results," Medical Decision Making, , vol. 25(2), pages 168-177, March.
  • Handle: RePEc:sae:medema:v:25:y:2005:i:2:p:168-177
    DOI: 10.1177/0272989X05275154
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X05275154
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X05275154?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. Dwivedi Alok Kumar & Mallawaarachchi Indika & Figueroa-Casas Juan B. & Morales Angel M. & Tarwater Patrick, 2015. "Multinomial Logistic Regression Approach for the Evaluation of Binary Diagnostic Test in Medical Research," Statistics in Transition New Series, Statistics Poland, vol. 16(2), pages 203-222, June.
    2. Angel M. Morales & Patrick Tarwater & Indika Mallawaarachchi & Alok Kumar Dwivedi & Juan B. Figueroa-Casas, 2015. "Multinomial logistic regression approach for the evaluation of binary diagnostic test in medical research," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(2), pages 203-222, June.
    3. Ying Huang & Eric Laber, 2016. "Personalized Evaluation of Biomarker Value: A Cost-Benefit Perspective," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 43-65, June.
    4. Alok Kumar Dwivedi & Indika Mallawaarachchi & Juan B. Figueroa-Casas & Angel M. Morales & Patrick Tarwater, 2015. "Multinomial Logistic Regression Approach For The Evaluation Of Binary Diagnostic Test In Medical Research," Statistics in Transition New Series, Polish Statistical Association, vol. 16(2), pages 203-222, June.

    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:sae:medema:v:25:y:2005:i:2:p:168-177. 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: SAGE Publications (email available below). General contact details of provider: .

    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.