Year: 2018
Type: article
Abstract: Covariate-adaptive randomisation has a long history of applications in clinical trials. Shao, Yu, and Zhong [(2010). A theory for testing hypotheses under covariate-adaptive randomization. Biometrika, 97, 347–360] and Shao and Yu [(2013). Validity of tests under covariate-adaptive biased coin randomization and generalized linear models. Biometrics, 69, 960–969] showed that the simple t-test is conservative under covariate-adaptive biased coin (CABC) randomisation in terms of type I error, and proposed a valid test using the bootstrap. Under a general additive model with CABC randomisation, we construct a calibrated t-test that shares the same property as the bootstrap method in Shao et al. (2010), but do not need large computation required by the bootstrap method. Some simulation results are presented to show the finite sample performance of the calibrated t-test.
Author: Ting Ye
Institution: University of Wisconsin–Madison
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2803901697