Ganju Jitendra, Julie Ma Guoguang
Gilead Sciences, Foster City, USA.
Stat Methods Med Res. 2017 Feb;26(1):64-74. doi: 10.1177/0962280214538016. Epub 2016 Sep 30.
The conventional approach to hypothesis testing for formal inference is to prespecify a single test statistic thought to be optimal. However, we usually have more than one test statistic in mind for testing the null hypothesis of no treatment effect but we do not know which one is the most powerful. Rather than relying on a single p-value, combining p-values from prespecified multiple test statistics can be used for inference. Combining functions include Fisher's combination test and the minimum p-value. Using randomization-based tests, the increase in power can be remarkable when compared with a single test and Simes's method. The versatility of the method is that it also applies when the number of covariates exceeds the number of observations. The increase in power is large enough to prefer combined p-values over a single p-value. The limitation is that the method does not provide an unbiased estimator of the treatment effect and does not apply to situations when the model includes treatment by covariate interaction.
用于形式推断的传统假设检验方法是预先指定一个被认为是最优的单一检验统计量。然而，在检验无治疗效果的零假设时，我们通常会想到不止一个检验统计量，但我们不知道哪一个最具功效。不是依赖单个p值，而是可以使用来自预先指定的多个检验统计量的组合p值进行推断。组合函数包括费舍尔组合检验和最小p值。使用基于随机化的检验，与单个检验和西姆斯方法相比，功效的提升可能会很显著。该方法的通用性在于，当协变量的数量超过观测值的数量时它也适用。功效的提升足够大，以至于更倾向于使用组合p值而非单个p值。其局限性在于该方法不能提供治疗效果的无偏估计量，并且不适用于模型包含治疗与协变量交互作用的情况。
