IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i13p1462-d579829.html
   My bibliography  Save this article

Confidence Intervals and Sample Size to Compare the Predictive Values of Two Diagnostic Tests

Author

Listed:
  • José Antonio Roldán-Nofuentes

    (Department of Statistics, School of Medicine, University of Granada, 18016 Granada, Spain)

  • Saad Bouh Regad

    (Epidemiology and Public Health Research Unit and URMCD, University of Nouakchott Alaasriya, BP 880 Nouakchott, Mauritania)

Abstract

A binary diagnostic test is a medical test that is applied to an individual in order to determine the presence or the absence of a certain disease and whose result can be positive or negative. A positive result indicates the presence of the disease, and a negative result indicates the absence. Positive and negative predictive values represent the accuracy of a binary diagnostic test when it is applied to a cohort of individuals, and they are measures of the clinical accuracy of the binary diagnostic test. In this manuscript, we study the comparison of the positive (negative) predictive values of two binary diagnostic tests subject to a paired design through confidence intervals. We have studied confidence intervals for the difference and for the ratio of the two positive (negative) predictive values. Simulation experiments have been carried out to study the asymptotic behavior of the confidence intervals, giving some general rules for application. We also study a method to calculate the sample size to compare the parameters using confidence intervals. We have written a program in R to solve the problems studied in this manuscript. The results have been applied to the diagnosis of colorectal cancer.

Suggested Citation

  • José Antonio Roldán-Nofuentes & Saad Bouh Regad, 2021. "Confidence Intervals and Sample Size to Compare the Predictive Values of Two Diagnostic Tests," Mathematics, MDPI, vol. 9(13), pages 1-19, June.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:13:p:1462-:d:579829
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/13/1462/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/13/1462/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Roldán Nofuentes, José Antonio & Luna del Castillo, Juan de Dios & Montero Alonso, Miguel Ángel, 2012. "Global hypothesis test to simultaneously compare the predictive values of two binary diagnostic tests," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1161-1173.
    2. Wendy Leisenring & Todd Alono & Margaret Sullivan Pepe, 2000. "Comparisons of Predictive Values of Binary Medical Diagnostic Tests for Paired Designs," Biometrics, The International Biometric Society, vol. 56(2), pages 345-351, June.
    Full references (including those not matched with items on IDEAS)

    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. Saad Bouh Regad & José Antonio Roldán-Nofuentes, 2021. "Global Hypothesis Test to Compare the Predictive Values of Diagnostic Tests Subject to a Case-Control Design," Mathematics, MDPI, vol. 9(6), pages 1-22, March.
    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. A. S. Hedayat & Junhui Wang & Tu Xu, 2015. "Minimum clinically important difference in medical studies," Biometrics, The International Biometric Society, vol. 71(1), pages 33-41, March.
    4. Xueyan Mei & Zelong Liu & Ayushi Singh & Marcia Lange & Priyanka Boddu & Jingqi Q. X. Gong & Justine Lee & Cody DeMarco & Chendi Cao & Samantha Platt & Ganesh Sivakumar & Benjamin Gross & Mingqian Hua, 2023. "Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. 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.
    6. Xingye Qiao & Yufeng Liu, 2009. "Adaptive Weighted Learning for Unbalanced Multicategory Classification," Biometrics, The International Biometric Society, vol. 65(1), pages 159-168, March.
    7. Robert H. Lyles & John M. Williamson & Hung-Mo Lin & Charles M. Heilig, 2005. "Extending McNemar's Test: Estimation and Inference When Paired Binary Outcome Data Are Misclassified," Biometrics, The International Biometric Society, vol. 61(1), pages 287-294, March.
    8. 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, Polish Statistical Association, vol. 16(2), pages 203-222, June.
    9. Scott H Lee & Matthew J Maenner & Charles M Heilig, 2019. "A comparison of machine learning algorithms for the surveillance of autism spectrum disorder," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-11, September.
    10. Peter H. Westfall & James F. Troendle & Gene Pennello, 2010. "Multiple McNemar Tests," Biometrics, The International Biometric Society, vol. 66(4), pages 1185-1191, December.

    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:gam:jmathe:v:9:y:2021:i:13:p:1462-:d:579829. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.