IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v39y2012i5p1037-1048.html
   My bibliography  Save this article

A generalization of the Grizzle model to the estimation of treatment effects in crossover trials with non-compliance

Author

Listed:
  • Ali Reza Soltanian
  • Soghrat Faghihzadeh

Abstract

Compliance with one specified dosing strategy of assigned treatments is a common problem in randomized drug clinical trials. Recently, there has been much interest in methods used for analysing treatment effects in randomized clinical trials that are subject to non-compliance. In this paper, we estimate and compare treatment effects based on the Grizzle model (GM) (ignorable non-compliance) as the custom model and the generalized Grizzle model (GGM) (non-ignorable non-compliance) as the new model. A real data set based on the treatment of knee osteoarthritis is used to compare these models. The results based on the likelihood ratio statistics and simulation study show the advantage of the proposed model (GGM) over the custom model (GGM).

Suggested Citation

  • Ali Reza Soltanian & Soghrat Faghihzadeh, 2012. "A generalization of the Grizzle model to the estimation of treatment effects in crossover trials with non-compliance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1037-1048, October.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1037-1048
    DOI: 10.1080/02664763.2011.634396
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2011.634396
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2011.634396?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. James Robins & Andrea Rotnitzky, 2004. "Estimation of treatment effects in randomised trials with non-compliance and a dichotomous outcome using structural mean models," Biometrika, Biometrika Trust, vol. 91(4), pages 763-783, December.
    2. Yau L.H.Y. & Little R.J., 2001. "Inference for the Complier-Average Causal Effect From Longitudinal Data Subject to Noncompliance and Missing Data, With Application to a Job Training Assessment for the Unemployed," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1232-1244, December.
    3. S. Vansteelandt & E. Goetghebeur, 2003. "Causal inference with generalized structural mean models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 817-835, November.
    4. Shigeyuki Matsui, 2005. "Stratified Analysis in Randomized Trials with Noncompliance," Biometrics, The International Biometric Society, vol. 61(3), pages 816-823, September.
    5. Ten Have, Thomas R. & Elliott, Michael R. & Joffe, Marshall & Zanutto, Elaine & Datto, Catherine, 2004. "Causal Models for Randomized Physician Encouragement Trials in Treating Primary Care Depression," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 16-25, January.
    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. Paul Clarke & Frank Windmeijer, 2009. "Identification of Causal Effects on Binary Outcomes Using Structural Mean Models," The Centre for Market and Public Organisation 09/217, The Centre for Market and Public Organisation, University of Bristol, UK.
    2. Imbens, Guido W., 2014. "Instrumental Variables: An Econometrician's Perspective," IZA Discussion Papers 8048, Institute of Labor Economics (IZA).
    3. Joffe Marshall M & Small Dylan & Ten Have Thomas & Brunelli Steve & Feldman Harold I, 2008. "Extended Instrumental Variables Estimation for Overall Effects," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-20, April.
    4. Paul S. Clarke & Frank Windmeijer, 2012. "Instrumental Variable Estimators for Binary Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1638-1652, December.
    5. Paul S. Clarke & Tom M. Palmer & Frank Windmeijer, 2011. "Estimating structural mean models with multiple instrumental variables using the generalised method of moments," CeMMAP working papers CWP28/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Linbo Wang & Xiang Meng & Thomas S. Richardson & James M. Robins, 2023. "Coherent modeling of longitudinal causal effects on binary outcomes," Biometrics, The International Biometric Society, vol. 79(2), pages 775-787, June.
    7. Ditte Nørbo Sørensen & Torben Martinussen & Eric Tchetgen Tchetgen, 2019. "A causal proportional hazards estimator under homogeneous or heterogeneous selection in an IV setting," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 639-659, October.
    8. He Jiwei & Stephens-Shields Alisa & Joffe Marshall, 2015. "Structural Nested Mean Models to Estimate the Effects of Time-Varying Treatments on Clustered Outcomes," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 203-222, November.
    9. Lui, Kung-Jong & Cumberland, William G., 2008. "Notes on estimation of proportion ratio under a non-compliance randomized trial with missing outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4325-4345, May.
    10. Mark van der Laan & Alan Hubbard & Nicholas Jewell, 2004. "Estimation of Treatment Effects in Randomized Trials with Noncompliance and a Dichotomous Outcome," U.C. Berkeley Division of Biostatistics Working Paper Series 1157, Berkeley Electronic Press.
    11. Murielle Bochud & Valentin Rousson, 2010. "Usefulness of Mendelian Randomization in Observational Epidemiology," IJERPH, MDPI, vol. 7(3), pages 1-18, February.
    12. Kern, Holger & Hainmueller, Jens, 2007. "Opium for the Masses: How Foreign Free Media Can Stabilize Authoritarian Regimes," MPRA Paper 2702, University Library of Munich, Germany.
    13. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    14. Han, Sukjin, 2021. "Identification in nonparametric models for dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 132-147.
    15. Bandyopadhyay, Uttam & Sarkar, Suman & Biswas, Atanu, 2022. "Sequential confidence interval for comparing two Bernoulli distributions in a non-conventional set-up," Statistics & Probability Letters, Elsevier, vol. 181(C).
    16. Steven Lehrer & Weili Ding, 2004. "Estimating Dynamic Treatment Effects from Project STAR," Econometric Society 2004 North American Summer Meetings 252, Econometric Society.
    17. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    18. Luca Zanin & Rosalba Radice & Giampiero Marra, 2013. "Estimating the Effect of Perceived Risk of Crime on Social Trust in the Presence of Endogeneity Bias," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 114(2), pages 523-547, November.
    19. Shosei Sakaguchi, 2021. "Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints," Papers 2106.05031, arXiv.org, revised Apr 2024.
    20. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022. "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers 2203.13887, arXiv.org, revised Jun 2023.

    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:japsta:v:39:y:2012:i:5:p:1037-1048. 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/CJAS20 .

    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.