IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v8y2012i2p1-20n6.html

Adjusting for Perception and Unmasking Effects in Longitudinal Clinical Trials

Author

Listed:
  • Hubbard Alan

    (University of California; Berkeley)

  • Jamshidian Farid

    (University of California; Berkeley)

  • Jewell Nicholas

    (University of California; Berkeley)

Abstract

A blinded clinical trial design requires masking of patients to prevent measurement of their outcome from being influenced by knowledge of treatment assignment. However, during the course of a trial, some patients may be practically unmasked either due to experiencing treatment related side effects in the treatment arm, or lack of efficacy in the placebo arm. In a recent paper, we introduced concepts of perception, unmasking, and placebo effects for point treatment studies. In this paper, we generalize these concepts to longitudinal studies, and use recent advancements in causal inference and semi-parametric efficient estimation to define and estimate perception and unmasking effects. This allows differentiation of the impact on measured outcomes of `early‘ versus `late‘ unmasking. In particular, two semi-parametric, substitution methods, one based only on the prediction model (G-computation) and an augmented version of that model for targeted bias-reduction (Targeted Maximum Likelihood Estimation; TMLE), are used for estimation of perception and treatment effects. We motivate our discussion by analyzing data from a recent longitudinal study on the effect of gabapentin on pain among diabetic patients experiencing painful neuropathy.

Suggested Citation

  • Hubbard Alan & Jamshidian Farid & Jewell Nicholas, 2012. "Adjusting for Perception and Unmasking Effects in Longitudinal Clinical Trials," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-20, December.
    • Handle: RePEc:bpj:ijbist:v:8:y:2012:i:2:p:1-20:n:6
    DOI: 10.2202/1557-4679.1376
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1376
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1376?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

    for a different version of it.

    References listed on IDEAS

    as
    1. van der Laan Mark J. & Rubin Daniel, 2006. "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-40, 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. Justin Whitehouse & Qizhao Chen & Morgane Austern & Vasilis Syrgkanis, 2025. "Inference on Optimal Policy Values and Other Irregular Functionals via Softmax Smoothing," Papers 2507.11780, arXiv.org, revised Mar 2026.
    2. Harsh Parikh & Carlos Varjao & Louise Xu & Eric Tchetgen Tchetgen, 2022. "Validating Causal Inference Methods," Papers 2202.04208, arXiv.org, revised Jul 2022.
    3. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
    4. Tran Linh & Petersen Maya & Schwab Joshua & van der Laan Mark J., 2023. "Robust variance estimation and inference for causal effect estimation," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-27, January.
    5. S Ariane Christie & Amanda S Conroy & Rachael A Callcut & Alan E Hubbard & Mitchell J Cohen, 2019. "Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-13, April.
    6. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    7. Michael Rosenblum & Nicholas P. Jewell & Mark van der Laan & Stephen Shiboski & Ariane van der Straten & Nancy Padian, 2009. "Analysing direct effects in randomized trials with secondary interventions: an application to human immunodeficiency virus prevention trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 443-465, April.
    8. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    9. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    10. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    11. Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.
    12. Jenna Wong & Daniel Prieto-Alhambra & Peter R. Rijnbeek & Rishi J. Desai & Jenna M. Reps & Sengwee Toh, 2022. "Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations," Drug Safety, Springer, vol. 45(5), pages 493-510, May.
    13. Jun Wang & Yahe Yu, 2024. "Improved estimation of average treatment effects under covariate‐adaptive randomization methods," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(2), pages 310-333, May.
    14. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    15. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    16. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    17. Stitelman Ori M & van der Laan Mark J., 2010. "Collaborative Targeted Maximum Likelihood for Time to Event Data," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-46, June.
    18. Chaffee Paul H. & van der Laan Mark J., 2012. "Targeted Maximum Likelihood Estimation for Dynamic Treatment Regimes in Sequentially Randomized Controlled Trials," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-32, June.
    19. Gruber Susan & van der Laan Mark J., 2010. "A Targeted Maximum Likelihood Estimator of a Causal Effect on a Bounded Continuous Outcome," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-18, August.
    20. Zhiwei Zhang & Richard M. Kotz & Chenguang Wang & Shiling Ruan & Martin Ho, 2013. "A Causal Model for Joint Evaluation of Placebo and Treatment-Specific Effects in Clinical Trials," Biometrics, The International Biometric Society, vol. 69(2), pages 318-327, June.

    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:bpj:ijbist:v:8:y:2012:i:2:p:1-20:n:6. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .

    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.