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Clinical trials and p-values, beware of the extremes.

机译：临床试验和p值，请提防极端情况。

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BACKGROUND: In randomized controlled trials, prior to statistical analysis, the data are checked for outliers and erroneous data. Statistical tests are, traditionally, not very good at distinguishing between errors and outliers, but they should be able to point out main endpoint results closer to expectation than compatible with random sampling. OBJECTIVES: To explain from hypothesized and published examples why extreme p-values like p>0.95 and p<0.0001 may indicate that sampling was not completely random. RESULTS: Extreme p-values can be readily observed in recent issues of high-impact journals. A p-value >0.95 literally means that we have a >95% chance of finding a result less close to expectation and, consequently, a <5% chance of finding a result this close or closer. Often in studies a statistical power of 80% is agreed upon, corresponding with a p-value of approximately 0.01. The ultimate p-value may then be a bit larger or smaller. However, a p-value much smaller than 0.01 will be rarely observed, because it would indicate that the study is overpowered. If the p-values can be assumed to follow a normal distribution around 0.01, then we will have a less than 5% chance of observing a p-value of <0.0001. CONCLUSIONS: In randomized controlled trials, main endpoint p-values larger than p=0.95 will be rare, because they would indicate similarities closer than compatible with a normal distribution of random data samples. Also very low p-values like p<0.0001 will be rarely encountered, because it would mean that the trial was overpowered and should have had a smaller sample size. It would seem appropriate, therefore, to require investigators to explain such results and to consider rejecting the research involved. So far, in randomized controlled trials the null-hypothesis is generally rejected at p<0.05. Perhaps we should consider rejecting the entire study if the main endpoint p-values are >0.95 or <0.0001.
机译：背景：在随机对照试验中，在进行统计分析之前，先检查数据中的异常值和错误数据。传统上，统计测试不能很好地区分错误和离群值，但与随机抽样不兼容，统计测试应能够指出更接近预期的主要终点结果。目的：从假设和公开的例子中解释为什么p> 0.95和p <0.0001这样的极端p值可能表明采样不是完全随机的。结果：在最近一期的高影响力期刊中，可以很容易地观察到极高的p值。 p值> 0.95字面上意味着我们有> 95％的机会找到不太接近期望的结果，因此，有<5％的机会找到了这个接近或接近的结果。在研究中，通常同​​意80％的统计功效，对应的p值约为0.01。最终的p值可能会更大或更小。但是，很少会观察到小于0.01的p值，因为这表明该研究过于强大。如果可以假设p值遵循0.01左右的正态分布，那么我们观察到p值<0.0001的机会将少于5％。结论：在随机对照试验中，大于p = 0.95的主要终点p值将很少见，因为它们表明相似性比与随机数据样本的正态分布的相容性更近。同样，很少会遇到像p <0.0001这样的非常低的p值，因为这将意味着该试验过于强大，并且应该具有较小的样本量。因此，要求研究者解释这些结果并考虑拒绝所涉及的研究似乎是适当的。到目前为止，在随机对照试验中，原假设通常在p <0.05时被拒绝。如果主要终点p值> 0.95或<0.0001，也许我们应该考虑拒绝整个研究。

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