Rejoinder to “Robustness of ANCOVA in randomized trials with unequal randomization” by Jonathan W. Bartlett

Wang, Bingkai; Ogburn, Elizabeth L.; Rosenblum, Michael

doi:10.1111/biom.13182

Biometrics

2019

DOI: 10.1111/biom.13182

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Rejoinder to “Robustness of ANCOVA in randomized trials with unequal randomization” by Jonathan W. Bartlett

Bingkai Wang

Elizabeth L. Ogburn

Michael Rosenblum

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2024

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Robustness of response-adaptive randomization

Ye,

Hu,

2024

Biometrics

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Doubly adaptive biased coin design (DBCD), a response-adaptive randomization scheme, aims to skew subject assignment probabilities based on accrued responses for ethical considerations. Recent years have seen substantial advances in understanding DBCD’s theoretical properties, assuming correct model specification for the responses. However, concerns have been raised about the impact of model misspecification on its design and analysis. In this paper, we assess the robustness to both design model misspecification and analysis model misspecification under DBCD. On one hand, we confirm that the consistency and asymptotic normality of the allocation proportions can be preserved, even when the responses follow a distribution other than the one imposed by the design model during the implementation of DBCD. On the other hand, we extensively investigate three commonly used linear regression models for estimating and inferring the treatment effect, namely difference-in-means, analysis of covariance (ANCOVA) I, and ANCOVA II. By allowing these regression models to be arbitrarily misspecified, thereby not reflecting the true data generating process, we derive the consistency and asymptotic normality of the treatment effect estimators evaluated from the three models. The asymptotic properties show that the ANCOVA II model, which takes covariate-by-treatment interaction terms into account, yields the most efficient estimator. These results can provide theoretical support for using DBCD in scenarios involving model misspecification, thereby promoting the widespread application of this randomization procedure.

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Robustness of response-adaptive randomization

Ye,

Hu,

2024

Biometrics

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Rejoinder to “Robustness of ANCOVA in randomized trials with unequal randomization” by Jonathan W. Bartlett

Cited by 1 publication

References 4 publications

Robustness of response-adaptive randomization

Robustness of response-adaptive randomization

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