Li Yang, Ma Wei, Qin Yichen, Hu Feifang
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China.
Institute of Statistics and Big Data, Renmin University of China, Beijing, China.
Stat Methods Med Res. 2021 Sep;30(9):2148-2164. doi: 10.1177/09622802211008206. Epub 2021 Apr 26.
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 -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.
尽管协变量自适应随机化在临床试验中被广泛使用，但人们对其统计推断的有效性仍表示担忧。在文献中，协变量自适应随机化下的推断性质主要是针对连续反应进行研究的；特别是，众所周知，用于治疗效果的常用双样本检验通常是保守的。在未对协变量进行调整的广义线性模型中也发现了这种无效检验的现象，而且由于第一类错误膨胀，有时情况更令人担忧。本研究的目的是检验广义线性模型和协变量自适应随机化下未调整的治疗效果检验。对于一大类协变量自适应随机化方法，我们在原假设下获得了检验统计量的渐近分布，并推导了检验是保守、有效或反保守的条件。详细讨论了几种常用的广义线性模型，如逻辑回归和泊松回归。还提出了一种调整方法，以根据渐近结果实现有效的检验规模。数值研究证实了理论结果，并证明了所提出调整方法的有效性。
