IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v35y2001i4p429-449.html
   My bibliography  Save this article

Describing heterogeneous effects in stratified ordinal contingency tables, with application to multi-center clinical trials

Author

Listed:
  • Hartzel, Jonathan
  • Liu, I-Ming
  • Agresti, Alan

Abstract

No abstract is available for this item.

Suggested Citation

  • Hartzel, Jonathan & Liu, I-Ming & Agresti, Alan, 2001. "Describing heterogeneous effects in stratified ordinal contingency tables, with application to multi-center clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 35(4), pages 429-449, February.
    • Handle: RePEc:eee:csdana:v:35:y:2001:i:4:p:429-449
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(00)00020-7
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Murray Aitkin, 1999. "A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models," Biometrics, The International Biometric Society, vol. 55(1), pages 117-128, March.
    2. Tutz, Gerhard & Hennevogl, Wolfgang, 1996. "Random effects in ordinal regression models," Computational Statistics & Data Analysis, Elsevier, vol. 22(5), pages 537-557, September.
    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. Yu, Dalei & Zhang, Xinyu & Yau, Kelvin K.W., 2013. "Information based model selection criteria for generalized linear mixed models with unknown variance component parameters," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 245-262.
    2. Andreas Groll & Gerhard Tutz, 2017. "Variable selection in discrete survival models including heterogeneity," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 305-338, April.
    3. Agresti, Alan & Caffo, Brian & Ohman-Strickland, Pamela, 2004. "Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 639-653, October.
    4. Tutz, Gerhard, 2004. "Generalized semiparametrically structured mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 777-800, July.

    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. Tutz, Gerhard, 2004. "Generalized semiparametrically structured mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 777-800, July.
    2. Tomasz Lenartowicz & Henryk Bujak & Marcin Przystalski & Inna Mashevska & Kamila Nowosad & Krzysztof Jończyk & Beata Feledyn-Szewczyk, 2024. "Assessment of Resistance of Barley Varieties to Diseases in Polish Organic Field Trials," Agriculture, MDPI, vol. 14(5), pages 1-11, May.
    3. Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re‐evaluation of random‐effects meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159, January.
    4. Paul S. Albert, 2007. "Random Effects Modeling Approaches for Estimating ROC Curves from Repeated Ordinal Tests without a Gold Standard," Biometrics, The International Biometric Society, vol. 63(2), pages 593-602, June.
    5. Getachew A. Dagne, 2016. "A growth mixture Tobit model: application to AIDS studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(7), pages 1174-1185, July.
    6. Z. Rezaei Ghahroodi & M. Ganjali, 2013. "A Bayesian approach for analysing longitudinal nominal outcomes using random coefficients transitional generalized logit model: an application to the labour force survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(7), pages 1425-1445, July.
    7. Marino, Maria Francesca & Alfó, Marco, 2016. "Gaussian quadrature approximations in mixed hidden Markov models for longitudinal data: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 193-209.
    8. Suzanne J Carroll & Michael J Dale & Theophile Niyonsenga & Anne W Taylor & Mark Daniel, 2020. "Associations between area socioeconomic status, individual mental health, physical activity, diet and change in cardiometabolic risk amongst a cohort of Australian adults: A longitudinal path analysis," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-16, May.
    9. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    10. Lluís Bermúdez & Dimitris Karlis & Isabel Morillo, 2020. "Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models," Risks, MDPI, vol. 8(1), pages 1-13, January.
    11. Marco Alfo & Giovanni Trovato & Robert J. Waldmann, 2008. "Testing for country heterogeneity in growth models using a finite mixture approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(4), pages 487-514.
    12. van den Berg, Gerard J. & Uhlendorff, Arne & Wolff, Joachim, 2015. "Under heavy pressure : intense monitoring and accumulation of sanctions for young welfare recipients in Germany," IAB-Discussion Paper 201534, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    13. Caliendo, Marco & Künn, Steffen & Uhlendorff, Arne, 2016. "Earnings exemptions for unemployed workers: The relationship between marginal employment, unemployment duration and job quality," Labour Economics, Elsevier, vol. 42(C), pages 177-193.
    14. Marco Alfò & Stefano Caiazza & Giovanni Trovato, 2005. "Extending a Logistic Approach to Risk Modeling through Semiparametric Mixing," Journal of Financial Services Research, Springer;Western Finance Association, vol. 28(1), pages 163-176, October.
    15. Antonello Maruotti & Pierfrancesco Alaimo Di Loro, 2023. "CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    16. Bruno Crepon & Marc Ferracci & Grégory Jolivet & Gerard Van Den Berg, 2010. "Analyzing the Anticipation of Treatments with Data on Notification Dates," Working Papers 2010-41, Center for Research in Economics and Statistics.
    17. Staudenmayer, John & Ruppert, David & Buonaccorsi, John P., 2008. "Density Estimation in the Presence of Heteroscedastic Measurement Error," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 726-736, June.
    18. S. Noorian & M. Ganjali & E. Bahrami Samani, 2016. "A Bayesian test of homogeneity of association parameter using transition modelling of longitudinal mixed responses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(10), pages 1850-1863, August.
    19. Caffo, Brian & An, Ming-Wen & Rohde, Charles, 2007. "Flexible random intercept models for binary outcomes using mixtures of normals," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5220-5235, July.
    20. repec:jss:jstsof:08:i09 is not listed on IDEAS
    21. Marco Alfò & Giovanni Trovato, 2004. "Semiparametric Mixture Models for Multivariate Count Data, with Application," CEIS Research Paper 51, Tor Vergata University, CEIS.

    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:35:y:2001:i:4:p:429-449. 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.