IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2008i1p27-37.html

Comparing multiple sensitivities and specificities with different diagnostic criteria: Applications to sexual abuse and sexual health research

Author

Listed:
  • Yu, Q.
  • Tang, W.
  • Ma, Y.
  • Gamble, S.A.
  • Tu, X.M.

Abstract

When comparing sensitivities and specificities from multiple diagnostic tests, particularly in biomedical research, the different test kits under study are applied to groups of subjects with the same disease status for a disease or medical condition under consideration. Although this process gives rise to clustered or correlated test outcomes, the associated inference issues are well recognized and have been widely discussed in the literature. In mental health and psychosocial research, sensitivity and specificity have also been widely used to study the reliability of instruments for diagnosing mental health and psychiatric conditions and assessing certain behavioral patterns. However, unlike biomedical applications, outcomes are often obtained under varying reference standards or different diagnostic criteria, precluding the application of existing methods for comparing multiple diagnostic tests to such a research setting. In this paper, we develop a new approach to address these problems (including that of missing data) by extending recent work on inference using inverse probability weighted estimates. The approach is illustrated with data from two studies in sexual abuse and health research as well as a limited simulation study, with the latter used to study the performance of the proposed procedure.

Suggested Citation

  • Yu, Q. & Tang, W. & Ma, Y. & Gamble, S.A. & Tu, X.M., 2008. "Comparing multiple sensitivities and specificities with different diagnostic criteria: Applications to sexual abuse and sexual health research," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 27-37, September.
    • Handle: RePEc:eee:csdana:v:53:y:2008:i:1:p:27-37
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00296-X
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Catania, J.A. & Binson, D. & Dolcini, M.M. & Stall, R. & Choi, K.-H. & Pollack, L.M. & Hudes, E.S. & Canchola, J. & Phillips, K. & Moskowitz, J.T. & Coates, T.J., 1995. "Risk factors for HIV and other sexually transmitted diseases and prevention practices among US heterosexual adults: Changes from 1990 to 1992," American Journal of Public Health, American Public Health Association, vol. 85(11), pages 1492-1499.
    2. Ivy Jansen & Geert Molenberghs & Marc Aerts & Herbert Thijs & Kristel Van Steen, 2003. "A Local Influence Approach Applied to Binary Data from a Psychiatric Study," Biometrics, The International Biometric Society, vol. 59(2), pages 410-419, June.
    3. 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. 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.
    2. 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.
    3. S. Eftekhari Mahabadi & M. Ganjali, 2012. "An index of local sensitivity to non-ignorability for parametric survival models with potential non-random missing covariate: an application to the SEER cancer registry data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2327-2348, July.
    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. Baojiang Chen & Xiao-Hua Zhou, 2011. "Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates," Biometrics, The International Biometric Society, vol. 67(3), pages 830-842, September.
    6. 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.
    7. Ivy Jansen & Geert Molenberghs, 2008. "A flexible marginal modelling strategy for non‐monotone missing data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 347-373, April.
    8. Wiens, Douglas P., 2010. "Robustness of design for the testing of lack of fit and for estimation in binary response models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3371-3378, December.
    9. Jansen, Ivy & Hens, Niel & Molenberghs, Geert & Aerts, Marc & Verbeke, Geert & Kenward, Michael G., 2006. "The nature of sensitivity in monotone missing not at random models," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 830-858, February.
    10. 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.
    11. Xingye Qiao & Yufeng Liu, 2009. "Adaptive Weighted Learning for Unbalanced Multicategory Classification," Biometrics, The International Biometric Society, vol. 65(1), pages 159-168, March.
    12. 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.
    13. Lutz Mattner & Frauke Mattner, 2013. "Confidence bounds for the sensitivity lack of a less specific diagnostic test, without gold standard," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(2), pages 239-263, February.
    14. Trias Wahyuni Rakhmawati & Geert Molenberghs & Geert Verbeke & Christel Faes, 2016. "Local influence diagnostics for incomplete overdispersed longitudinal counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1722-1737, July.
    15. Ivy Jansen & Ann Van den Troost & Geert Molenberghs & Ad A. Vermulst & Jan R. M. Gerris, 2006. "Modeling Partially Incomplete Marital Satisfaction Data," Sociological Methods & Research, , vol. 35(1), pages 113-136, August.
    16. Marta Malagón & Elizabeth Alwers & Lia Oliver & Sara Ramió-Pujol & Mireia Sánchez-Vizcaino & Joan Amoedo & Salomé de Cambra & Mariona Serra-Pagès & Antoni Castells & Xavier Aldeguer & Jesús Garcia-Gil, 2023. "Clinical performance study of a fecal bacterial signature test for colorectal cancer screening," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-12, November.
    17. 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.
    18. 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.
    19. O'Hara Hines, R.J. & Hines, W.G.S., 2007. "Covariance miss-specification and the local influence approach in sensitivity analyses of longitudinal data with drop-outs," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5537-5546, August.
    20. 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.

    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:eee:csdana:v:53:y:2008:i:1:p:27-37. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.