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

Adam Kapelner1, Abba Krieger2

  • 1Department of Mathematics, Queens College, CUNY, Queens, New York, USA.

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Summary
This summary is machine-generated.

This study introduces a dynamic allocation method for sequential trials, improving power and efficiency by learning from subject responses. The approach enhances treatment effect measurement and is available via an R package.

Keywords:
clinical trialscovariate and response adaptive randomizationcrowdsourcing experimentationmatchingsequential experiments

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Experimental Design

Background:

  • Sequential randomized trials require efficient methods for estimating average treatment effects.
  • Existing allocation procedures may not fully leverage subject response data for improved efficiency.

Purpose of the Study:

  • To propose a novel dynamic allocation procedure for sequential randomized trials.
  • To enhance the power and efficiency of measuring average treatment effects by utilizing prior subject responses.
  • To develop robust estimators and an exact test for the average treatment effect.

Main Methods:

  • Subjects are allocated sequentially, either randomized or dynamically paired to existing subjects.
  • A dynamic matching criterion iteratively identifies important covariates influencing response.
  • Estimators for average treatment effect and an exact test are developed.

Main Results:

  • The proposed dynamic allocation procedure demonstrates increased efficiency and power compared to other methods.
  • Illustrative examples using simulated data and a clinical trial dataset confirm the method's advantages.
  • The R package "SeqExpMatch" facilitates practical application of the procedure.

Conclusions:

  • Dynamic allocation in sequential trials significantly improves statistical power and efficiency.
  • The method effectively utilizes subject covariate and response data for optimal treatment effect estimation.
  • The "SeqExpMatch" R package provides a valuable tool for researchers and practitioners in clinical trial design.