IDEAS home Printed from https://ideas.repec.org/a/eee/deveng/v1y2016icp12-25.html
   My bibliography  Save this article

The pursuit of balance in sequential randomized trials

Author

Listed:
  • Guiteras, Raymond P.
  • Levine, David I.
  • Polley, Thomas H.

Abstract

In many randomized trials, subjects enter the sample sequentially. Because the covariates for all units are not known in advance, standard methods of stratification do not apply. We describe and assess the method of DA-optimal sequential allocation (Atkinson, 1982) for balancing stratification covariates across treatment arms. We provide simulation evidence that the method can provide substantial improvements in precision over commonly employed alternatives. We also describe our experience implementing the method in a field trial of a clean water and handwashing intervention in Dhaka, Bangladesh, the first time the method has been used. We provide advice and software for future researchers.

Suggested Citation

  • Guiteras, Raymond P. & Levine, David I. & Polley, Thomas H., 2016. "The pursuit of balance in sequential randomized trials," Development Engineering, Elsevier, vol. 1(C), pages 12-25.
    • Handle: RePEc:eee:deveng:v:1:y:2016:i:c:p:12-25
    DOI: 10.1016/j.deveng.2015.11.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2352728515300580
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.deveng.2015.11.001?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. Silvio S. Zocchi & Anthony C. Atkinson, 1999. "Optimum Experimental Designs for Multinomial Logistic Models," Biometrics, The International Biometric Society, vol. 55(2), pages 437-444, June.
    2. Lori Beaman & Jeremy Magruder, 2012. "Who Gets the Job Referral? Evidence from a Social Networks Experiment," American Economic Review, American Economic Association, vol. 102(7), pages 3574-3593, December.
    3. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2018. "Inference Under Covariate-Adaptive Randomization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1784-1796, October.
    4. James Berry & Greg Fischer & Raymond Guiteras, 2020. "Eliciting and Utilizing Willingness to Pay: Evidence from Field Trials in Northern Ghana," Journal of Political Economy, University of Chicago Press, vol. 128(4), pages 1436-1473.
    5. Anthony C. Atkinson, 2002. "The comparison of designs for sequential clinical trials with covariate information," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 349-373, June.
    6. Dimitris Bertsimas & Mac Johnson & Nathan Kallus, 2015. "The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples," Operations Research, INFORMS, vol. 63(4), pages 868-876, August.
    7. Jun Shao & Xinxin Yu & Bob Zhong, 2010. "A theory for testing hypotheses under covariate-adaptive randomization," Biometrika, Biometrika Trust, vol. 97(2), pages 347-360.
    8. Jun Shao & Xinxin Yu, 2013. "Validity of Tests under Covariate-Adaptive Biased Coin Randomization and Generalized Linear Models," Biometrics, The International Biometric Society, vol. 69(4), pages 960-969, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Massimiliano Russo & Steffen Ventz & Victoria Wang & Lorenzo Trippa, 2023. "Inference in response‐adaptive clinical trials when the enrolled population varies over time," Biometrics, The International Biometric Society, vol. 79(1), pages 381-393, March.

    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. Tong Wang & Wei Ma, 2021. "The impact of misclassification on covariate‐adaptive randomized clinical trials," Biometrics, The International Biometric Society, vol. 77(2), pages 451-464, June.
    2. Jiang, Liang & Phillips, Peter C.B. & Tao, Yubo & Zhang, Yichong, 2023. "Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 234(2), pages 758-776.
    3. Liang Jiang & Oliver B. Linton & Haihan Tang & Yichong Zhang, 2022. "Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance," Papers 2201.13004, arXiv.org, revised Jun 2023.
    4. Yichong Zhang & Xin Zheng, 2020. "Quantile treatment effects and bootstrap inference under covariate‐adaptive randomization," Quantitative Economics, Econometric Society, vol. 11(3), pages 957-982, July.
    5. Ting Ye & Jun Shao, 2020. "Robust tests for treatment effect in survival analysis under covariate‐adaptive randomization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1301-1323, December.
    6. Raymond P. Guiteras & B. Kelsey Jack, 2014. "Incentives, Selection and Productivity in Labor Markets: Evidence from Rural Malawi," NBER Working Papers 19825, National Bureau of Economic Research, Inc.
    7. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2018. "Inference Under Covariate-Adaptive Randomization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1784-1796, October.
    8. Atkinson, Anthony C. & Biswas, Atanu, 2017. "Optimal response and covariate-adaptive biased-coin designs for clinical trials with continuous multivariate or longitudinal responses," LSE Research Online Documents on Economics 66761, London School of Economics and Political Science, LSE Library.
    9. Nikhil Bhat & Vivek F. Farias & Ciamac C. Moallemi & Deeksha Sinha, 2020. "Near-Optimal A-B Testing," Management Science, INFORMS, vol. 66(10), pages 4477-4495, October.
    10. Young, Alwyn, 2024. "Asymptotically robust permutation-based randomization confidence intervals for parametric OLS regression," LSE Research Online Documents on Economics 120933, London School of Economics and Political Science, LSE Library.
    11. Atkinson, Anthony C. & Biswas, Atanu, 2017. "Optimal response and covariate-adaptive biased-coin designs for clinical trials with continuous multivariate or longitudinal responses," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 297-310.
    12. Vittorio Bassi & Aisha Nansamba, 2022. "Screening and Signalling Non-Cognitive Skills: Experimental Evidence from Uganda," The Economic Journal, Royal Economic Society, vol. 132(642), pages 471-511.
    13. Yujia Gu & Hanzhong Liu & Wei Ma, 2023. "Regression‐based multiple treatment effect estimation under covariate‐adaptive randomization," Biometrics, The International Biometric Society, vol. 79(4), pages 2869-2880, December.
    14. Ruoxuan Xiong & Susan Athey & Mohsen Bayati & Guido Imbens, 2019. "Optimal Experimental Design for Staggered Rollouts," Papers 1911.03764, arXiv.org, revised Sep 2023.
    15. Young, Alwyn, 2024. "Asymptotically robust permutation-based randomization confidence intervals for parametric OLS regression," European Economic Review, Elsevier, vol. 163(C).
    16. Belmiro P. M. Duarte & Anthony C. Atkinson & David Pedrosa & Marlena van Munster, 2024. "Compound Optimum Designs for Clinical Trials in Personalized Medicine," Mathematics, MDPI, vol. 12(19), pages 1-20, September.
    17. Liang Jiang & Liyao Li & Ke Miao & Yichong Zhang, 2023. "Adjustment with Many Regressors Under Covariate-Adaptive Randomizations," Papers 2304.08184, arXiv.org, revised Feb 2024.
    18. Sharon L. Lohr & Xiaoshu Zhu, 2017. "Randomized Sequential Individual Assignment in Social Experiments: Evaluating the Design Options Prospectively," Sociological Methods & Research, , vol. 46(4), pages 1049-1075, November.
    19. Idais, Osama, 2020. "Locally optimal designs for multivariate generalized linear models," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    20. Afridi, Farzana & Dhillon, Amrita & Sharma, Swati, 2015. "Social Networks and Labour Productivity: A Survey of Recent Theory and Evidence," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 50(1), pages 25-42.

    More about this item

    Keywords

    Stratification; Sequential randomization; Design of Experiments;
    All these keywords.

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

    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:eee:deveng:v:1:y:2016:i:c:p:12-25. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/development-engineering .

    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.