Covariate–Adaptive Randomization with Unequal Allocation

2015 ◽  
pp. 189-216
Keyword(s):  
Adaptive Randomization ◽  
Unequal Allocation

scholarly journals Testing for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates

2021 ◽  
pp. 096228022110082
Author(s):  
Yang Li ◽  
Wei Ma ◽  
Yichen Qin ◽  
Feifang Hu
Keyword(s):  
Generalized Linear Models ◽  
Treatment Effect ◽  
Linear Models ◽  
Type I Error ◽  
Type I ◽  
Test Statistic ◽  
Asymptotic Results ◽  
Adaptive Randomization ◽  
Adjustment Method ◽  
Inflated Type

Concerns have been expressed over the validity of statistical inference under covariate-adaptive randomization despite the extensive use in clinical trials. In the literature, the inferential properties under covariate-adaptive randomization have been mainly studied for continuous responses; in particular, it is well known that the usual two-sample t-test for treatment effect is typically conservative. This phenomenon of invalid tests has also been found for generalized linear models without adjusting for the covariates and are sometimes more worrisome due to inflated Type I error. The purpose of this study is to examine the unadjusted test for treatment effect under generalized linear models and covariate-adaptive randomization. For a large class of covariate-adaptive randomization methods, we obtain the asymptotic distribution of the test statistic under the null hypothesis and derive the conditions under which the test is conservative, valid, or anti-conservative. Several commonly used generalized linear models, such as logistic regression and Poisson regression, are discussed in detail. An adjustment method is also proposed to achieve a valid size based on the asymptotic results. Numerical studies confirm the theoretical findings and demonstrate the effectiveness of the proposed adjustment method.

scholarly journals Outcome-adaptive randomization for a delayed outcome with a short-term predictor: imputation-based designs

2014 ◽  
Vol 33 (23) ◽  
pp. 4029-4042 ◽  
Author(s):  
Mi-Ok Kim ◽  
Chunyan Liu ◽  
Feifang Hu ◽  
J. Jack Lee
Keyword(s):  
Short Term ◽  
Adaptive Randomization

scholarly journals Estimation accuracy under covariate-adaptive randomization procedures

2017 ◽  
Vol 11 (1) ◽  
pp. 1180-1206 ◽  
Author(s):  
Alessandro Baldi Antognini ◽  
Maroussa Zagoraiou
Keyword(s):  
Estimation Accuracy ◽  
Adaptive Randomization

Improved adaptive randomization strategies for a seamless Phase I/II dose-finding design

2018 ◽  
Vol 29 (2) ◽  
pp. 333-347 ◽  
Author(s):  
Donglin Yan ◽  
Nolan A. Wages ◽  
Emily V. Dressler
Keyword(s):  
Phase I ◽  
Dose Finding ◽  
Adaptive Randomization

OLS and 2SLS in Randomized and Conditionally Randomized Experiments

2018 ◽  
Vol 238 (3-4) ◽  
pp. 243-293 ◽  
Author(s):  
Jason Ansel ◽  
Han Hong ◽  
and Jessie Li
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Randomized Experiments ◽  
Adaptive Randomization ◽  
Working Paper ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Effect Parameter ◽  
Multiple Treatments ◽  
Local Average

Abstract We investigate estimation and inference of the (local) average treatment effect parameter when a binary instrumental variable is generated by a randomized or conditionally randomized experiment. Under i.i.d. sampling, we show that adding covariates and their interactions with the instrument will weakly improve estimation precision of the (local) average treatment effect, but the robust OLS (2SLS) standard errors will no longer be valid. We provide an analytic correction that is easy to implement and demonstrate through Monte Carlo simulations and an empirical application the interacted estimator’s efficiency gains over the unadjusted estimator and the uninteracted covariate adjusted estimator. We also generalize our results to covariate adaptive randomization where the treatment assignment is not i.i.d., thus extending the recent contributions of Bugni, F., I.A. Canay, A.M. Shaikh (2017a), Inference Under Covariate-Adaptive Randomization. Working Paper and Bugni, F., I.A. Canay, A.M. Shaikh (2017b), Inference Under Covariate-Adaptive Randomization with Multiple Treatments. Working Paper to allow for the case of non-compliance.

Comments on Berry et al.’s response-adaptive randomization platform trial for Ebola

2016 ◽  
Vol 13 (5) ◽  
pp. 566-567
Author(s):  
Erica H Brittain ◽  
Michael A Proschan
Keyword(s):  
Adaptive Randomization
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