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A matching procedure for sequential experiments that iteratively learns which covariates improve power

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  • Adam Kapelner
  • Abba Krieger

Abstract

We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials exploiting some subjects' previous assessed responses. Subjects arrive sequentially and are either randomized or paired to a previously randomized subject and administered the alternate treatment. The pairing is made via a dynamic matching criterion that iteratively learns which specific covariates are important to the response. We develop estimators for the average treatment effect as well as an exact test. We illustrate our method's increase in efficiency and power over other allocation procedures in both simulated scenarios and a clinical trial dataset. An R package “SeqExpMatch” for use by practitioners is available on CRAN.

Suggested Citation

  • Adam Kapelner & Abba Krieger, 2023. "A matching procedure for sequential experiments that iteratively learns which covariates improve power," Biometrics, The International Biometric Society, vol. 79(1), pages 216-229, March.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:1:p:216-229
    DOI: 10.1111/biom.13561
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    References listed on IDEAS

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    1. Kari Lock Morgan & Donald B. Rubin, 2015. "Rerandomization to Balance Tiers of Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1412-1421, December.
    2. Chandler, Dana & Kapelner, Adam, 2013. "Breaking monotony with meaning: Motivation in crowdsourcing markets," Journal of Economic Behavior & Organization, Elsevier, vol. 90(C), pages 123-133.
    3. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    4. Quan Zhou & Philip A Ernst & Kari Lock Morgan & Donald B Rubin & Anru Zhang, 2018. "Sequential rerandomization," Biometrika, Biometrika Trust, vol. 105(3), pages 745-752.
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