IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v49y2000i3p287-296.html
   My bibliography  Save this article

Using a Box–Cox transformation in the analysis of longitudinal data with incomplete responses

Author

Listed:
  • S. R. Lipsitz
  • J. Ibrahim
  • G. Molenberghs

Abstract

We analyse longitudinal data on CD4 cell counts from patients who participated in clinical trials that compared two therapeutic treatments: zidovudine and didanosine. The investigators were interested in modelling the CD4 cell count as a function of treatment, age at base‐line and disease stage at base‐line. Serious concerns can be raised about the normality assumption of CD4 cell counts that is implicit in many methods and therefore an analysis may have to start with a transformation. Instead of assuming that we know the transformation (e.g. logarithmic) that makes the outcome normal and linearly related to the covariates, we estimate the transformation, by using maximum likelihood, within the Box–Cox family. There has been considerable work on the Box–Cox transformation for univariate regression models. Here, we discuss the Box–Cox transformation for longitudinal regression models when the outcome can be missing over time, and we also implement a maximization method for the likelihood, assumming that the missing data are missing at random.

Suggested Citation

  • S. R. Lipsitz & J. Ibrahim & G. Molenberghs, 2000. "Using a Box–Cox transformation in the analysis of longitudinal data with incomplete responses," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 287-296.
    • Handle: RePEc:bla:jorssc:v:49:y:2000:i:3:p:287-296
    DOI: 10.1111/1467-9876.00192
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9876.00192
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-9876.00192?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tan, W. D. & Gan, F. F. & Chang, T. C., 2004. "Using normal quantile plot to select an appropriate transformation to achieve normality," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 609-619, April.
    2. Gurka, Matthew J. & Edwards, Lloyd J. & Nylander-French, Leena, 2007. "Testing transformations for the linear mixed model," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4297-4307, May.
    3. Jiang, Jiakun & Lin, Huazhen & Zhong, Qingzhi & Li, Yi, 2022. "Analysis of multivariate non-gaussian functional data: A semiparametric latent process approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

    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:49:y:2000:i:3:p:287-296. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.