Ganju Jitendra, Yu Xinxin, Ma Guoguang Julie
Gilead Sciences, Foster City, CA 94404, USA.
Pharm Stat. 2013 Sep-Oct;12(5):282-90. doi: 10.1002/pst.1582. Epub 2013 Aug 6.
Formal inference in randomized clinical trials is based on controlling the type I error rate associated with a single pre-specified statistic. The deficiency of using just one method of analysis is that it depends on assumptions that may not be met. For robust inference, we propose pre-specifying multiple test statistics and relying on the minimum p-value for testing the null hypothesis of no treatment effect. The null hypothesis associated with the various test statistics is that the treatment groups are indistinguishable. The critical value for hypothesis testing comes from permutation distributions. Rejection of the null hypothesis when the smallest p-value is less than the critical value controls the type I error rate at its designated value. Even if one of the candidate test statistics has low power, the adverse effect on the power of the minimum p-value statistic is not much. Its use is illustrated with examples. We conclude that it is better to rely on the minimum p-value rather than a single statistic particularly when that single statistic is the logrank test, because of the cost and complexity of many survival trials.
随机临床试验中的形式推断基于控制与单个预先指定的统计量相关的I型错误率。仅使用一种分析方法的不足之处在于它依赖于可能无法满足的假设。为了进行稳健的推断，我们建议预先指定多个检验统计量，并依靠最小p值来检验无治疗效果的零假设。与各种检验统计量相关的零假设是治疗组无法区分。假设检验的临界值来自排列分布。当最小p值小于临界值时拒绝零假设可将I型错误率控制在其指定值。即使候选检验统计量之一的功效较低，对最小p值统计量的功效的不利影响也不大。通过示例说明了它的用法。我们得出结论，尤其是当单个统计量是对数秩检验时，最好依靠最小p值而不是单个统计量，因为许多生存试验的成本和复杂性。
