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Hauptverfasser:	Ma, Xingjian, Liu, Yang
Format:	Preprint
Veröffentlicht:	2023
Schlagworte:	Applications Methodology
Online-Zugang:	https://arxiv.org/abs/2311.17605
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_version_	1866929638588022784
author	Ma, Xingjian Liu, Yang
author_facet	Ma, Xingjian Liu, Yang
contents	Multi-arm randomization has increasingly widespread applications recently and it is also crucial to ensure that the distributions of important observed covariates as well as the potential unobserved covariates are similar and comparable among all the treatment. However, the theoretical properties of unobserved covariates imbalance in multi-arm randomization with unequal allocation ratio remains unknown. In this paper, we give a general framework analysing the moments and distributions of unobserved covariates imbalance and apply them into different procedures including complete randomization (CR), stratified permuted block (STR-PB) and covariate-adaptive randomization (CAR). The general procedures of multi-arm STR-PB and CAR with unequal allocation ratio are also proposed. In addition, we introduce the concept of entropy to measure the correlation between discrete covariates and verify that we could utilize the correlation to select observed covariates to help better balance the unobserved covariates.
format	Preprint
id	arxiv_https___arxiv_org_abs_2311_17605
institution	arXiv
publishDate	2023
record_format	arxiv
spellingShingle	Improving the Balance of Unobserved Covariates From Information Theory in Multi-Arm Randomization with Unequal Allocation Ratio Ma, Xingjian Liu, Yang Applications Methodology Multi-arm randomization has increasingly widespread applications recently and it is also crucial to ensure that the distributions of important observed covariates as well as the potential unobserved covariates are similar and comparable among all the treatment. However, the theoretical properties of unobserved covariates imbalance in multi-arm randomization with unequal allocation ratio remains unknown. In this paper, we give a general framework analysing the moments and distributions of unobserved covariates imbalance and apply them into different procedures including complete randomization (CR), stratified permuted block (STR-PB) and covariate-adaptive randomization (CAR). The general procedures of multi-arm STR-PB and CAR with unequal allocation ratio are also proposed. In addition, we introduce the concept of entropy to measure the correlation between discrete covariates and verify that we could utilize the correlation to select observed covariates to help better balance the unobserved covariates.
title	Improving the Balance of Unobserved Covariates From Information Theory in Multi-Arm Randomization with Unequal Allocation Ratio
topic	Applications Methodology
url	https://arxiv.org/abs/2311.17605

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