IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i3p1896-1907.html
   My bibliography  Save this article

Contrasting principal stratum and hypothetical strategy estimands in multi‐period crossover trials with incomplete data

Author

Listed:
  • John N.S. Matthews
  • Sofia Bazakou
  • Robin Henderson
  • Linda D. Sharples

Abstract

Complete case analyses of complete crossover designs provide an opportunity to make comparisons based on patients who can tolerate all treatments. It is argued that this provides a means of estimating a principal stratum strategy estimand, something which is difficult to do in parallel group trials. While some trial users will consider this a relevant aim, others may be interested in hypothetical strategy estimands, that is, the effect that would be found if all patients completed the trial. Whether these estimands differ importantly is a question of interest to the different users of the trial results. This paper derives the difference between principal stratum strategy and hypothetical strategy estimands, where the former is estimated by a complete‐case analysis of the crossover design, and a model for the dropout process is assumed. Complete crossover designs, that is, those where all treatments appear in all sequences, and which compare t treatments over p periods with respect to a continuous outcome are considered. Numerical results are presented for Williams designs with four and six periods. Results from a trial of obstructive sleep apnoea‐hypopnoea (TOMADO) are also used for illustration. The results demonstrate that the percentage difference between the estimands is modest, exceeding 5% only when the trial has been severely affected by dropouts or if the within‐subject correlation is low.

Suggested Citation

  • John N.S. Matthews & Sofia Bazakou & Robin Henderson & Linda D. Sharples, 2023. "Contrasting principal stratum and hypothetical strategy estimands in multi‐period crossover trials with incomplete data," Biometrics, The International Biometric Society, vol. 79(3), pages 1896-1907, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:1896-1907
    DOI: 10.1111/biom.13777
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13777
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13777?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
    ---><---

    References listed on IDEAS

    as
    1. Bate, S.T. & Godolphin, E.J. & Godolphin, J.D., 2008. "Choosing cross-over designs when few subjects are available," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1572-1586, January.
    2. Godolphin, J.D., 2006. "The specification of rank reducing observation sets in experimental design," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1862-1874, December.
    Full references (including those not matched with items on IDEAS)

    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. Godolphin, J.D. & Warren, H.R., 2014. "An efficient procedure for the avoidance of disconnected incomplete block designs," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1134-1146.
    2. Godolphin, P.J. & Godolphin, E.J., 2019. "Robust assessment of two-treatment higher-order cross-over designs against missing values," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 31-45.
    3. Godolphin, J.D., 2009. "New formulations for recursive residuals as a diagnostic tool in the fixed-effects linear model with design matrices of arbitrary rank," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2119-2128, April.
    4. Bate, S.T. & Godolphin, E.J. & Godolphin, J.D., 2008. "Choosing cross-over designs when few subjects are available," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1572-1586, January.

    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:biomet:v:79:y:2023:i:3:p:1896-1907. 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: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.