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A design for testing clinical strategies: biased adaptive within‐subject randomization

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  • P. W. Lavori
  • R. Dawson

Abstract

We propose a method for assigning treatment in clinical trials, called the ‘biased coin adaptive within‐subject’ (BCAWS) design: during the course of follow‐up, the subject's response to a treatment is used to influence the future treatment, through a ‘biased coin’ algorithm. This design results in treatment patterns that are closer to actual clinical practice and may be more acceptable to patients with chronic disease than the usual fixed trial regimens, which often suffer from drop‐out and non‐adherence. In this work, we show how to use the BCAWS design to compare treatment strategies, and we provide a simple example to illustrate the method.

Suggested Citation

  • P. W. Lavori & R. Dawson, 2000. "A design for testing clinical strategies: biased adaptive within‐subject randomization," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(1), pages 29-38.
    • Handle: RePEc:bla:jorssa:v:163:y:2000:i:1:p:29-38
    DOI: 10.1111/1467-985X.00154
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    Cited by:

    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. Ying Liu & Yuanjia Wang & Donglin Zeng, 2017. "Sequential multiple assignment randomization trials with enrichment design," Biometrics, The International Biometric Society, vol. 73(2), pages 378-390, June.
    3. Kristin A. Linn & Eric B. Laber & Leonard A. Stefanski, 2017. "Interactive -Learning for Quantiles," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 638-649, April.
    4. van der Laan Mark J., 2010. "Targeted Maximum Likelihood Based Causal Inference: Part I," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-45, February.
    5. 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.
    6. Stephens Alisa & Keele Luke & Joffe Marshall, 2016. "Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1-17, September.
    7. Armando Turchetta & Erica E. M. Moodie & David A. Stephens & Sylvie D. Lambert, 2023. "Bayesian sample size calculations for comparing two strategies in SMART studies," Biometrics, The International Biometric Society, vol. 79(3), pages 2489-2502, September.
    8. Giorgos Bakoyannis, 2023. "Estimating optimal individualized treatment rules with multistate processes," Biometrics, The International Biometric Society, vol. 79(4), pages 2830-2842, December.
    9. Bibhas Chakraborty & Eric B. Laber & Yingqi Zhao, 2013. "Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m-Out-of-n Bootstrap Scheme," Biometrics, The International Biometric Society, vol. 69(3), pages 714-723, September.
    10. Tang, Xinyu & Melguizo, Maria, 2015. "DTR: An R Package for Estimation and Comparison of Survival Outcomes of Dynamic Treatment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i07).
    11. Neugebauer Romain & Schmittdiel Julie A. & Adams Alyce S. & Grant Richard W. & van der Laan Mark J., 2017. "Identification of the Joint Effect of a Dynamic Treatment Intervention and a Stochastic Monitoring Intervention Under the No Direct Effect Assumption," Journal of Causal Inference, De Gruyter, vol. 5(1), pages 1-44, March.
    12. Lina M. Montoya & Michael R. Kosorok & Elvin H. Geng & Joshua Schwab & Thomas A. Odeny & Maya L. Petersen, 2023. "Efficient and robust approaches for analysis of sequential multiple assignment randomized trials: Illustration using the ADAPT‐R trial," Biometrics, The International Biometric Society, vol. 79(3), pages 2577-2591, September.
    13. Yan‐Cheng Chao & Thomas M. Braun & Roy N. Tamura & Kelley M. Kidwell, 2020. "A Bayesian group sequential small n sequential multiple‐assignment randomized trial," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 663-680, June.
    14. Stephens Alisa & Joffe Marshall & Keele Luke, 2016. "Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1, September.
    15. David Benkeser & Keith Horvath & Cathy J. Reback & Joshua Rusow & Michael Hudgens, 2020. "Design and Analysis Considerations for a Sequentially Randomized HIV Prevention Trial," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(3), pages 446-467, December.

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