IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v63y2014i1p141-158.html
   My bibliography  Save this article

Time varying frailty models and the estimation of heterogeneities in transmission of infectious diseases

Author

Listed:
  • Steffen Unkel
  • C. Paddy Farrington
  • Heather J. Whitaker
  • Richard Pebody

Abstract

No abstract is available for this item.

Suggested Citation

  • Steffen Unkel & C. Paddy Farrington & Heather J. Whitaker & Richard Pebody, 2014. "Time varying frailty models and the estimation of heterogeneities in transmission of infectious diseases," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 141-158, January.
    • Handle: RePEc:bla:jorssc:v:63:y:2014:i:1:p:141-158
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssc.12033
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. C. P. Farrington & M. N. Kanaan & N. J. Gay, 2001. "Estimation of the basic reproduction number for infectious diseases from age‐stratified serological survey data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 251-292.
    2. Farrington, C. Paddy & Whitaker, Heather J., 2005. "Contact Surface Models for Infectious Diseases: Estimation From Serologic Survey Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 370-379, June.
    3. Florin Vaida & Suzette Blanchard, 2005. "Conditional Akaike information for mixed-effects models," Biometrika, Biometrika Trust, vol. 92(2), pages 351-370, June.
    4. C. Paddy Farrington & Steffen Unkel & Karim Anaya-Izquierdo, 2012. "The relative frailty variance and shared frailty models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(4), pages 673-696, 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. Sean Yiu & Vernon T. Farewell & Brian D. M. Tom, 2017. "Exploring the existence of a stayer population with mover–stayer counting process models: application to joint damage in psoriatic arthritis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 669-690, August.
    2. Effraimidis, Georgios, 2016. "Nonparametric Identification of a Time-Varying Frailty Model," DaCHE discussion papers 2016:6, University of Southern Denmark, Dache - Danish Centre for Health Economics.
    3. Steven Abrams & Marc Aerts & Geert Molenberghs & Niel Hens, 2017. "Parametric overdispersed frailty models for current status data," Biometrics, The International Biometric Society, vol. 73(4), pages 1388-1400, December.

    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. Steven Abrams & Marc Aerts & Geert Molenberghs & Niel Hens, 2017. "Parametric overdispersed frailty models for current status data," Biometrics, The International Biometric Society, vol. 73(4), pages 1388-1400, December.
    2. Nele Goeyvaerts & Niel Hens & Benson Ogunjimi & Marc Aerts & Ziv Shkedy & Pierre Van Damme & Philippe Beutels, 2010. "Estimating infectious disease parameters from data on social contacts and serological status," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 255-277, March.
    3. H. J. Whitaker & C. P. Farrington, 2004. "Infections with Varying Contact Rates: Application to Varicella," Biometrics, The International Biometric Society, vol. 60(3), pages 615-623, September.
    4. Bijlsma Ineke & van den Brakel Jan & van der Velden Rolf & Allen Jim, 2020. "Estimating Literacy Levels at a Detailed Regional Level: an Application Using Dutch Data," Journal of Official Statistics, Sciendo, vol. 36(2), pages 251-274, June.
    5. Jie Huang & Haiming Zhou & Nader Ebrahimi, 2022. "Bayesian Bivariate Cure Rate Models Using Copula Functions," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(3), pages 1-9, May.
    6. Kubokawa, Tatsuya & Nagashima, Bui, 2012. "Parametric bootstrap methods for bias correction in linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 1-16.
    7. Kimberly M. Thompson, 2016. "Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis," Risk Analysis, John Wiley & Sons, vol. 36(7), pages 1383-1403, July.
    8. Myung-Jae Hwang & Jong-Hun Kim & Hae-Kwan Cheong, 2020. "Short-Term Impacts of Ambient Air Pollution on Health-Related Quality of Life: A Korea Health Panel Survey Study," IJERPH, MDPI, vol. 17(23), pages 1-11, December.
    9. Abhik Ghosh & Magne Thoresen, 2018. "Non-concave penalization in linear mixed-effect models and regularized selection of fixed effects," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(2), pages 179-210, April.
    10. Lahrouz, A. & El Mahjour, H. & Settati, A. & Bernoussi, A., 2018. "Dynamics and optimal control of a non-linear epidemic model with relapse and cure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 299-317.
    11. J. N. K. Rao, 2015. "Inferential issues in model-based small area estimation: some new developments," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 491-510, December.
    12. Colin Griesbach & Andreas Groll & Elisabeth Bergherr, 2021. "Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
    13. Marco Barnabani, 2019. "An F -type multiple testing approach for assessing randomness of linear mixed models," Econometrics Working Papers Archive 2019_09, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    14. Marco Barnabani, 2015. "A parametric test to discriminate between a linear regression model and a linear latent growth model," Econometrics Working Papers Archive 2015_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    15. Charlotte Articus & Jan Pablo Burgard, 2014. "A Finite Mixture Fay Herriot-type model for estimating regional rental prices in Germany," Research Papers in Economics 2014-14, University of Trier, Department of Economics.
    16. Kubokawa, Tatsuya, 2011. "Conditional and unconditional methods for selecting variables in linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 641-660, March.
    17. Simona Buscemi & Antonella Plaia, 2020. "Model selection in linear mixed-effect models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 529-575, December.
    18. Yuki Kawakubo & Tatsuya Kubokawa & Muni S. Srivastava, 2018. "A Variant of AIC Based on the Bayesian Marginal Likelihood," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 60-84, May.
    19. Denis Federiakin, 2020. "Investigating The Cross-National Comparability Of Testing Using Response Times," HSE Working papers WP BRP 57/EDU/2020, National Research University Higher School of Economics.
    20. Cantoni, Eva & Jacot, Nadège & Ghisletta, Paolo, 2024. "Review and comparison of measures of explained variation and model selection in linear mixed-effects models," Econometrics and Statistics, Elsevier, vol. 29(C), pages 150-168.

    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:bla:jorssc:v:63:y:2014:i:1:p:141-158. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.