School of Population & Public Health, University of British Columbia, 2206 East Mall, Vancouver, BC, V6T 1Z3, Canada.
Trials. 2023 Jan 17;24(1):34. doi: 10.1186/s13063-023-07092-8.
Performing a sample size calculation for a randomized controlled trial requires specifying an assumed benefit (that is, the mean improvement in outcomes due to the intervention) and a target power. There is a widespread belief that judgments about the minimum important difference should be used when setting the assumed benefit and thus the sample size. This belief is misguided - when the purpose of the trial is to test the null hypothesis of no treatment benefit, the only role that the minimum important difference should be given is in determining whether the sample size should be zero, that is, whether the trial should be conducted at all.The true power of the trial depends on the true benefit, so the calculated sample size will result in a true power close to the target power used in the calculation only if the assumed benefit is close to the true benefit. Hence, the assumed benefit should be set to a value that is considered a realistic estimate of the true benefit. If a trial designed using a realistic value for the assumed benefit is unlikely to demonstrate that a meaningful benefit exists, the trial should not be conducted. Any attempt to reconcile discrepancies between the realistic estimate of benefit and the minimum important difference when setting the assumed benefit merely conflates a valid sample size calculation with one based on faulty inputs and leads to a true power that fails to match the target power.When calculating sample size, trial designers should focus efforts on determining reasonable estimates of the true benefit, not on what magnitude of benefit is judged important.
进行随机对照试验的样本量计算需要指定一个假设的益处（即干预导致的结果平均改善）和目标效力。人们普遍认为，在设定假设益处和样本量时，应该使用最小重要差异的判断。这种信念是有误导性的 - 当试验的目的是检验无治疗益处的零假设时，最小重要差异唯一的作用应该是确定样本量是否应为零，即试验是否应该进行。试验的真实效力取决于真实益处，因此只有当假设益处接近计算中使用的真实益处时，计算出的样本量才会导致真实效力接近目标效力。因此，假设益处应设定为被认为是真实益处的现实估计值。如果使用假设益处的现实值设计的试验不太可能证明存在有意义的益处，则不应进行该试验。当设定假设益处时，试图调和益处的现实估计值和最小重要差异之间的差异，只会将有效的样本量计算与基于错误输入的计算混为一谈，并导致真实效力与目标效力不匹配。在计算样本量时，试验设计者应专注于确定真实益处的合理估计值，而不是判断多大程度的益处是重要的。
