Validity of Tests under Covariate‐Adaptive Biased Coin Randomization and Generalized Linear Models

DOI10.1111/biom.12062zbMath1419.62442OpenAlexW1553818256WikidataQ46084064 ScholiaQ46084064MaRDI QIDQ5408016

Xinxin Yu, Jun Shao

Publication date: 8 April 2014

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.12062

zbMATH Keywords

clinical trialstype I errorbiased coinefficiency of testsrobustness against model misspecificationcovariate adaptive randomization

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Parametric hypothesis testing (62F03) Generalized linear models (logistic models) (62J12) Sequential statistical design (62L05)

Related Items (10)

Testing hypotheses under covariate-adaptive randomisation and additive models ⋮ Inference after covariate-adaptive randomisation: aspects of methodology and theory ⋮ The impact of misclassification on covariate‐adaptive randomized clinical trials ⋮ Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials ⋮ Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations ⋮ Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Covariate Adjustment ⋮ The impacts of unobserved covariates on covariate-adaptive randomized experiments ⋮ Statistical Inference for Covariate-Adaptive Randomization Procedures ⋮ Conditioning Efron's biased coin design to ensure final balance ⋮ Randomization tests in recursive response-adaptive randomization procedures

Cites Work

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• The adaptive biased coin design for sequential experiments
• A more powerful test for comparing two Poisson means
• A theory for testing hypotheses under covariate-adaptive randomization
• Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates
• Tests for no treatment effect in randomized clinical trials
• An Application of an Urn Model to the Design of Sequential Controlled Clinical Trials
• Forcing a sequential experiment to be balanced

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