IDEAS home Printed from https://ideas.repec.org/a/taf/gnstxx/v20y2008i6p459-481.html
   My bibliography  Save this article

Covariate-adjusted linear mixed effects model with an application to longitudinal data

Author

Listed:
  • Danh Nguyen
  • Damla şentürk
  • Raymond Carroll

Abstract

Linear mixed effects (LME) models are useful for longitudinal data/repeated measurements. We propose a new class of covariate-adjusted LME models for longitudinal data that nonparametrically adjusts for a normalising covariate. The proposed approach involves fitting a parametric LME model to the data after adjusting for the nonparametric effects of a baseline confounding covariate. In particular, the effect of the observable covariate on the response and predictors of the LME model is modelled nonparametrically via smooth unknown functions. In addition to covariate-adjusted estimation of fixed/population parameters and random effects, an estimation procedure for the variance components is also developed. Numerical properties of the proposed estimators are investigated with simulation studies. The consistency and convergence rates of the proposed estimators are also established. An application to a longitudinal data set on calcium absorption, accounting for baseline distortion from body mass index, illustrates the proposed methodology.

Suggested Citation

  • Danh Nguyen & Damla şentürk & Raymond Carroll, 2008. "Covariate-adjusted linear mixed effects model with an application to longitudinal data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(6), pages 459-481.
    • Handle: RePEc:taf:gnstxx:v:20:y:2008:i:6:p:459-481
    DOI: 10.1080/10485250802226435
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10485250802226435
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10485250802226435?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. Stephen J. Iturria & Raymond J. Carroll & David Firth, 1999. "Polynomial Regression and Estimating Functions in the Presence of Multiplicative Measurement Error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 547-561.
    2. Hulin Wu & Hua Liang, 2004. "Backfitting Random Varying‐Coefficient Models with Time‐Dependent Smoothing Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(1), pages 3-19, March.
    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. Jun Zhang & Junpeng Zhu & Yan Zhou & Xia Cui & Tao Lu, 2020. "Multiplicative regression models with distortion measurement errors," Statistical Papers, Springer, vol. 61(5), pages 2031-2057, October.
    2. Jun Zhang & Yiping Yang & Gaorong Li, 2020. "Logarithmic calibration for multiplicative distortion measurement errors regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(4), pages 462-488, November.
    3. Jun Zhang & Nanguang Zhou & Zipeng Sun & Gaorong Li & Zhenghong Wei, 2016. "Statistical inference on restricted partial linear regression models with partial distortion measurement errors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 304-331, November.
    4. Zhang, Jun & Feng, Zhenghui & Zhou, Bu, 2014. "A revisit to correlation analysis for distortion measurement error data," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 116-129.

    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. Xue, Liugen & Xue, Dong, 2011. "Empirical likelihood for semiparametric regression model with missing response data," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 723-740, April.
    2. Shuang Wu & Hans-Georg Müller, 2011. "Response-Adaptive Regression for Longitudinal Data," Biometrics, The International Biometric Society, vol. 67(3), pages 852-860, September.
    3. Elena Biewen & Sandra Nolte & Martin Rosemann, 2008. "Multiplicative Measurement Error and the Simulation Extrapolation Method," IAW Discussion Papers 39, Institut für Angewandte Wirtschaftsforschung (IAW).
    4. Wang-Li Xu & Li-Xing Zhu, 2009. "A goodness-of-fit test for a varying-coefficients model in longitudinal studies," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(4), pages 427-440.
    5. You, Jinhong & Chen, Gemai, 2006. "Estimation of a semiparametric varying-coefficient partially linear errors-in-variables model," Journal of Multivariate Analysis, Elsevier, vol. 97(2), pages 324-341, February.
    6. Veerabhadran Baladandayuthapani & Bani K. Mallick & Mee Young Hong & Joanne R. Lupton & Nancy D. Turner & Raymond J. Carroll, 2008. "Bayesian Hierarchical Spatially Correlated Functional Data Analysis with Application to Colon Carcinogenesis," Biometrics, The International Biometric Society, vol. 64(1), pages 64-73, March.
    7. Arellano-Valle, Reinaldo B. & Bolfarine, Heleno & Gasco, Loreta, 2002. "Measurement Error Models with Nonconstant Covariance Matrices," Journal of Multivariate Analysis, Elsevier, vol. 82(2), pages 395-415, August.

    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:gnstxx:v:20:y:2008:i:6:p:459-481. 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/GNST20 .

    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.