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Testing hypotheses under covariate-adaptive randomisation and additive models

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  • Ting Ye

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.

Suggested Citation

  • Ting Ye, 2018. "Testing hypotheses under covariate-adaptive randomisation and additive models," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 2(1), pages 96-101, January.
    • Handle: RePEc:taf:tstfxx:v:2:y:2018:i:1:p:96-101
    DOI: 10.1080/24754269.2018.1477005
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    Cited by:

    1. Jiang, Liang & Phillips, Peter C.B. & Tao, Yubo & Zhang, Yichong, 2023. "Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 234(2), pages 758-776.
    2. Ting Ye & Jun Shao, 2020. "Robust tests for treatment effect in survival analysis under covariate‐adaptive randomization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1301-1323, December.
    3. Liang Jiang & Oliver B. Linton & Haihan Tang & Yichong Zhang, 2022. "Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance," Papers 2201.13004, arXiv.org, revised Jun 2023.

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