IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v39y2012i6p1151-1175.html
   My bibliography  Save this article

Screening for prostate cancer using multivariate mixed-effects models

Author

Listed:
  • Christopher H. Morrell
  • Larry J. Brant
  • Shan Sheng
  • E. Jeffrey Metter

Abstract

Using several variables known to be related to prostate cancer, a multivariate classification method is developed to predict the onset of clinical prostate cancer. A multivariate mixed-effects model is used to describe longitudinal changes in prostate-specific antigen (PSA), a free testosterone index (FTI), and body mass index (BMI) before any clinical evidence of prostate cancer. The patterns of change in these three variables are allowed to vary depending on whether the subject develops prostate cancer or not and the severity of the prostate cancer at diagnosis. An application of Bayes’ theorem provides posterior probabilities that we use to predict whether an individual will develop prostate cancer and, if so, whether it is a high-risk or a low-risk cancer. The classification rule is applied sequentially one multivariate observation at a time until the subject is classified as a cancer case or until the last observation has been used. We perform the analyses using each of the three variables individually, combined together in pairs, and all three variables together in one analysis. We compare the classification results among the various analyses and a simulation study demonstrates how the sensitivity of prediction changes with respect to the number and type of variables used in the prediction process.

Suggested Citation

  • Christopher H. Morrell & Larry J. Brant & Shan Sheng & E. Jeffrey Metter, 2012. "Screening for prostate cancer using multivariate mixed-effects models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1151-1175, November.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1151-1175
    DOI: 10.1080/02664763.2011.644523
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2011.644523
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2011.644523?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
    ---><---

    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. Skates S. J & Pauler D. K & Jacobs I. J, 2001. "Screening Based on the Risk of Cancer Calculation From Bayesian Hierarchical Changepoint and Mixture Models of Longitudinal Markers," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 429-439, June.
    2. Minzhi Liu & Jeremy M. G. Taylor & Thomas R. Belin, 2000. "Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies," Biometrics, The International Biometric Society, vol. 56(4), pages 1157-1163, December.
    3. Steffen Fieuws & Geert Verbeke, 2006. "Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles," Biometrics, The International Biometric Society, vol. 62(2), pages 424-431, June.
    4. Inoue, Lurdes Y.T. & Etzioni, Ruth & Morrell, Christopher & Muller, Peter, 2008. "Modeling Disease Progression With Longitudinal Markers," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 259-270, March.
    5. Larry J. Brant & Shan L. Sheng & Christopher H. Morrell & Geert N. Verbeke & Emmanuel Lesaffre & H. Ballentine Carter, 2003. "Screening for prostate cancer by using random‐effects models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(1), pages 51-62, February.
    6. Jason Roy & Xihong Lin, 2000. "Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes," Biometrics, The International Biometric Society, vol. 56(4), pages 1047-1054, December.
    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. Margaux Delporte & Steffen Fieuws & Geert Molenberghs & Geert Verbeke & Simeon Situma Wanyama & Elpis Hatziagorou & Christiane De Boeck, 2022. "A joint normal‐binary (probit) model," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 37-51, December.
    2. Carles Serrat & Montserrat Ru� & Carmen Armero & Xavier Piulachs & H�ctor Perpi��n & Anabel Forte & �lvaro P�ez & Guadalupe G�mez, 2015. "Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1223-1239, June.
    3. Ian J C MacCormick & Bryan M Williams & Yalin Zheng & Kun Li & Baidaa Al-Bander & Silvester Czanner & Rob Cheeseman & Colin E Willoughby & Emery N Brown & George L Spaeth & Gabriela Czanner, 2019. "Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-20, January.

    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. Carles Serrat & Montserrat Ru� & Carmen Armero & Xavier Piulachs & H�ctor Perpi��n & Anabel Forte & �lvaro P�ez & Guadalupe G�mez, 2015. "Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1223-1239, June.
    2. Taban Baghfalaki & Mojtaba Ganjali, 2020. "A transition model for analyzing multivariate longitudinal data using Gaussian copula approach," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 169-223, June.
    3. Benjamin E. Leiby & Mary D. Sammel & Thomas R. Ten Have & Kevin G. Lynch, 2009. "Identification of multivariate responders and non‐responders by using Bayesian growth curve latent class models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 505-524, September.
    4. Shi, Peng, 2012. "Multivariate longitudinal modeling of insurance company expenses," Insurance: Mathematics and Economics, Elsevier, vol. 51(1), pages 204-215.
    5. Chuan Hong & Yang Ning & Peng Wei & Ying Cao & Yong Chen, 2017. "A semiparametric model for vQTL mapping," Biometrics, The International Biometric Society, vol. 73(2), pages 571-581, June.
    6. Jeonghye Choi & David R. Bell & Leonard M. Lodish, 2012. "Traditional and IS-Enabled Customer Acquisition on the Internet," Management Science, INFORMS, vol. 58(4), pages 754-769, April.
    7. Proust-Lima, Cécile & Joly, Pierre & Dartigues, Jean-François & Jacqmin-Gadda, Hélène, 2009. "Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1142-1154, February.
    8. Brisa N. Sánchez & Shan Kang & Bhramar Mukherjee, 2012. "A Latent Variable Approach to Study Gene–Environment Interactions in the Presence of Multiple Correlated Exposures," Biometrics, The International Biometric Society, vol. 68(2), pages 466-476, June.
    9. Padayachee Trishanta & Khamiakova Tatsiana & Shkedy Ziv & Burzykowski Tomasz & Salo Perttu & Perola Markus, 2019. "A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(2), pages 1-13, April.
    10. Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2014. "Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 146-162.
    11. Chiara Brombin & Luigi Salmaso & Lara Fontanella & Luigi Ippoliti, 2015. "Nonparametric combination-based tests in dynamic shape analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(4), pages 460-484, December.
    12. Ling Zhou & Huazhen Lin & Xinyuan Song & Yi Li, 2014. "Selection of Latent Variables for Multiple Mixed-outcome Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1064-1082, December.
    13. Margaux Delporte & Steffen Fieuws & Geert Molenberghs & Geert Verbeke & Simeon Situma Wanyama & Elpis Hatziagorou & Christiane De Boeck, 2022. "A joint normal‐binary (probit) model," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 37-51, December.
    14. David B. Dunson & M. Watson & Jack A. Taylor, 2003. "Bayesian Latent Variable Models for Median Regression on Multiple Outcomes," Biometrics, The International Biometric Society, vol. 59(2), pages 296-304, June.
    15. Congran Yu & Wenwen Guo & Xinyuan Song & Hengjian Cui, 2023. "Feature screening with latent responses," Biometrics, The International Biometric Society, vol. 79(2), pages 878-890, June.
    16. Stuart R. Lipsitz & Garrett M. Fitzmaurice & Roger D. Weiss, 2020. "Using Multiple Imputation with GEE with Non-monotone Missing Longitudinal Binary Outcomes," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 890-904, December.
    17. Mahdiyeh, Zahra & Kazemi, Iraj, 2019. "An innovative strategy on the construction of multivariate multimodal linear mixed-effects models," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    18. Ming Ouyang & Xinyuan Song, 2020. "Bayesian Local Influence of Generalized Failure Time Models with Latent Variables and Multivariate Censored Data," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 298-316, July.
    19. Ziyue Liu & Anne R. Cappola & Leslie J. Crofford & Wensheng Guo, 2014. "Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 108-118, March.
    20. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.

    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:taf:japsta:v:39:y:2012:i:6:p:1151-1175. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.