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The pursuit of balance in sequential randomized trials

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
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    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.

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    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

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