Centre for Biostatistics, Manchester Academic Health Science Centre, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK.
Cochrane Gynaecology and Fertility, The University of Auckland, Auckland City Hospital, Auckland, New Zealand.
Hum Reprod. 2022 May 3;37(5):895-901. doi: 10.1093/humrep/deac030.
Infertility randomized controlled trials (RCTs) are often too small to detect realistic treatment effects. Large observational studies have been proposed as a solution. However, this strategy threatens to weaken the evidence base further, because non-random assignment to treatments makes it impossible to distinguish effects of treatment from confounding factors. Alternative solutions are required. Power in an RCT can be increased by adjusting for prespecified, prognostic covariates when performing statistical analysis, and if stratified randomization or minimization has been used, it is essential to adjust in order to get the correct answer. We present data showing that this simple, free and frequently necessary strategy for increasing power is seldom employed, even in trials appearing in leading journals. We use this article to motivate a pedagogical discussion and provide a worked example. While covariate adjustment cannot solve the problem of underpowered trials outright, there is an imperative to use sound methodology to maximize the information each trial yields.
不孕不育的随机对照试验（RCT）通常规模太小，无法检测到实际的治疗效果。因此，有人提议进行大规模观察性研究。然而，这种策略可能会进一步削弱证据基础，因为非随机分组会使治疗效果与混杂因素无法区分。因此需要替代解决方案。在进行统计分析时，通过调整预设的预后协变量，可以增加 RCT 的效力。如果使用分层随机化或最小化，则必须进行调整以得出正确的答案。我们提供的数据表明，这种简单、免费且经常必要的增加效力的策略很少被采用，即使是在主要期刊上发表的试验也是如此。我们使用这篇文章来激发教学讨论并提供一个实例。虽然协变量调整不能完全解决效力不足的试验问题，但必须使用合理的方法最大限度地利用每个试验提供的信息。
