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

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 random...

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Veröffentlicht in:	Biometrics 2023-03, Vol.79 (1), p.216-229
Hauptverfasser:	Kapelner, Adam, Krieger, Abba
Format:	Artikel
Sprache:	eng
Schlagworte:	Clinical trials covariate and response adaptive randomization crowdsourcing experimentation Matching sequential experiments
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container_title	Biometrics
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creator	Kapelner, Adam Krieger, Abba
description	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.
doi_str_mv	10.1111/biom.13561
format	Article
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subjects	Clinical trials covariate and response adaptive randomization crowdsourcing experimentation Matching sequential experiments
title	A matching procedure for sequential experiments that iteratively learns which covariates improve power
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