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以错误发现率控制探索显著性AI 解读

When testing a single hypothesis, it is common knowledge that increasing the sample size after nonsignificant results and repeating the hypothesis test several times at unadjusted critical levels inflates the overall Type I error rate severely. In contrast, if a large number of hypotheses are tested controlling the False Discovery Rate, such “hunting for significance” has asymptotically no impact on the error rate. More specifically, if the sample size is increased for all…

通过序贯检验一致改进 Bonferroni 类检验AI 解读

We propose a new method to uniformly improve cutoff tests (as, e.g., the Bonferroni test) by replacing them with a sequential testing procedure. The proposed procedure provides a uniform improvement—that is, it rejects the null hypothesis at least in all cases where the original procedure does, has level α, and never requires a sample size larger than that of the original procedure. Information on the often-unknown joint distribution of the test statistics across the…

递归组合检验AI 解读

We present a method that extends the flexibility of adaptive designs to the number of interim analyses and to the choice of decision boundaries. At each stage of the trial, the design of the next stage can be determined using all of the information gathered so far. Additionally, one can specify the next stage as the final stage or can plan a further interim analysis. The method is based on a recursive application of the two-stage combination tests for p values. The crucial…