IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v1y2013i2p193-207n2.html
   My bibliography  Save this article

Analysis of Subgroup Data of Clinical Trials

Author

Listed:
  • Tsai Kao-Tai

    (Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA)

  • Peace Karl

    (Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA)

Abstract

Large randomized controlled clinical trials are the gold standard to evaluate and compare the effects of treatments. It is common practice for investigators to explore and even attempt to compare treatments, beyond the first round of primary analyses, for various subsets of the study populations based on scientific or clinical interests to take advantage of the potentially rich information contained in the clinical database. Although subjects are randomized to treatment groups in clinical trials, this does not imply the same degree of randomization among sub-populations of the original trials. Therefore, comparisons of treatments in sub-populations may not produce fair and unbiased results without properly addressing this issue. Covariate adjustments in regression analysis and propensity score matching are commonly used to address the non-randomized nature of the sub-populations issue with various degrees of success. However, further improvements to these methods are still possible. In this article, we propose an analysis strategy that shows improvement to conventional methods. Treatment effects and their differences are estimated after adjustment for background imbalances. Treatment groups are then compared using confidence intervals whose limits are determined using the Robbins–Monro stochastic approximation. Data from a recent clinical trial are used to illustrate the methodology.

Suggested Citation

  • Tsai Kao-Tai & Peace Karl, 2013. "Analysis of Subgroup Data of Clinical Trials," Journal of Causal Inference, De Gruyter, vol. 1(2), pages 193-207, September.
  • Handle: RePEc:bpj:causin:v:1:y:2013:i:2:p:193-207:n:2
    DOI: 10.1515/jci-2012-0008
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2012-0008
    Download Restriction: no

    File URL: https://libkey.io/10.1515/jci-2012-0008?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. Erica E. M. Moodie & Thomas S. Richardson & David A. Stephens, 2007. "Demystifying Optimal Dynamic Treatment Regimes," Biometrics, The International Biometric Society, vol. 63(2), pages 447-455, June.
    2. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    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. Q. Clairon & R. Henderson & N. J. Young & E. D. Wilson & C. J. Taylor, 2021. "Adaptive treatment and robust control," Biometrics, The International Biometric Society, vol. 77(1), pages 223-236, March.
    2. Luo, Yu & Graham, Daniel J. & McCoy, Emma J., 2023. "Semiparametric Bayesian doubly robust causal estimation," LSE Research Online Documents on Economics 117944, London School of Economics and Political Science, LSE Library.
    3. Rich Benjamin & Moodie Erica E. M. & A. Stephens David, 2016. "Influence Re-weighted G-Estimation," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 157-177, May.
    4. Lingyun Lyu & Yu Cheng & Abdus S. Wahed, 2023. "Imputation‐based Q‐learning for optimizing dynamic treatment regimes with right‐censored survival outcome," Biometrics, The International Biometric Society, vol. 79(4), pages 3676-3689, December.
    5. Peng Wu & Donglin Zeng & Haoda Fu & Yuanjia Wang, 2020. "On using electronic health records to improve optimal treatment rules in randomized trials," Biometrics, The International Biometric Society, vol. 76(4), pages 1075-1086, December.
    6. Xiaofei Bai & Anastasios A. Tsiatis & Wenbin Lu & Rui Song, 2017. "Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 585-604, October.
    7. Qizhao Chen & Morgane Austern & Vasilis Syrgkanis, 2023. "Inference on Optimal Dynamic Policies via Softmax Approximation," Papers 2303.04416, arXiv.org, revised Dec 2023.
    8. Wei Liu & Zhiwei Zhang & Lei Nie & Guoxing Soon, 2017. "A Case Study in Personalized Medicine: Rilpivirine Versus Efavirenz for Treatment-Naive HIV Patients," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1381-1392, October.
    9. Sies Aniek & Van Mechelen Iven, 2017. "Comparing Four Methods for Estimating Tree-Based Treatment Regimes," The International Journal of Biostatistics, De Gruyter, vol. 13(1), pages 1-20, May.
    10. Hongming Pu & Bo Zhang, 2021. "Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 318-345, April.
    11. Jin Wang & Donglin Zeng & D. Y. Lin, 2022. "Semiparametric single-index models for optimal treatment regimens with censored outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 744-763, October.
    12. Shonosuke Sugasawa & Hisashi Noma, 2021. "Efficient screening of predictive biomarkers for individual treatment selection," Biometrics, The International Biometric Society, vol. 77(1), pages 249-257, March.
    13. Jingxiang Chen & Yufeng Liu & Donglin Zeng & Rui Song & Yingqi Zhao & Michael R. Kosorok, 2016. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 942-947, July.
    14. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    15. Han, Sukjin, 2021. "Identification in nonparametric models for dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 132-147.
    16. Durlauf, Steven N. & Navarro, Salvador & Rivers, David A., 2016. "Model uncertainty and the effect of shall-issue right-to-carry laws on crime," European Economic Review, Elsevier, vol. 81(C), pages 32-67.
    17. Kastoryano, Stephen, 2024. "Biological, Behavioural and Spurious Selection on the Kidney Transplant Waitlist," IZA Discussion Papers 16995, Institute of Labor Economics (IZA).
    18. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    19. Yufan Zhao & Donglin Zeng & Mark A. Socinski & Michael R. Kosorok, 2011. "Reinforcement Learning Strategies for Clinical Trials in Nonsmall Cell Lung Cancer," Biometrics, The International Biometric Society, vol. 67(4), pages 1422-1433, December.
    20. Anders Bredahl Kock & Martin Thyrsgaard, 2017. "Optimal sequential treatment allocation," Papers 1705.09952, arXiv.org, revised Aug 2018.

    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:causin:v:1:y:2013:i:2:p:193-207:n:2. 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.degruyter.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.