A matching procedure for sequential experiments that iteratively learns which covariates improve power.
clinical trials
covariate and response adaptive randomization
crowdsourcing experimentation
matching
sequential experiments
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
Mar 2023
Mar 2023
Historique:
revised:
09
06
2021
received:
14
10
2020
accepted:
07
09
2021
pubmed:
19
9
2021
medline:
19
9
2021
entrez:
18
9
2021
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
216-229Subventions
Organisme : United States-Israel Binational Science Foundation
ID : 2018112
Informations de copyright
© 2021 The International Biometric Society.
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