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首页> 外文期刊>Annals of epidemiology >Randomized controlled trials with time-to-event outcomes: how much does prespecified covariate adjustment increase power?
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Randomized controlled trials with time-to-event outcomes: how much does prespecified covariate adjustment increase power?

机译：具有事件发生时间结果的随机对照试验：预先设定的协变量调整能增加多少功效？

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PURPOSE: We evaluated the effects of various strategies of covariate adjustment on type I error, power, and potential reduction in sample size in randomized controlled trials (RCTs) with time-to-event outcomes. METHODS: We used Cox models in simulated data sets with different treatment effects (hazard ratios [HRs] = 1, 1.4, and 1.7), covariate effects (HRs = 1, 2, and 5), covariate prevalences (10% and 50%), and censoring levels (no, low, and high). Treatment and a single covariate were dichotomous. We examined the sample size that gives the same power as an unadjusted analysis for three strategies: prespecified, significant predictive, and significant imbalance. RESULTS: Type I error generally was at the nominal level. The power to detect a true treatment effect was greater with adjusted than unadjusted analyses, especially with prespecified and significant-predictive strategies. Potential reductions in sample size with a covariate HR between 2 and 5 were between 15% and 44% (covariate prevalence 50%) and between 4% and 12% (covariate prevalence 10%). The significant-imbalance strategy yielded small reductions. The reduction was greater with stronger covariate effects, but was independent of treatment effect, sample size, and censoring level. CONCLUSIONS: Adjustment for one predictive baseline characteristic yields greater power to detect a true treatment effect than unadjusted analysis, without inflation of type I error and with potentially moderate reductions in sample size. Analysis of RCTs with time-to-event outcomes should adjust for predictive covariates.
机译：目的：我们评估了具有时间事件结果的随机对照试验（RCT）中协变量调整的各种策略对I型误差，功效和样本量潜在减少的影响。方法：我们在模拟数据集中使用Cox模型，具有不同的治疗效果（危险比[HRs] = 1、1.4和1.7），协变量效果（HRs = 1、2和5），协变量患病率（10％和50％） ）和检查级别（无，低和高）。治疗和单一协变量是二分法。我们针对三种策略检查了样本量，该样本量具有与未经调整的分析相同的功效：预定，显着预测和显着失衡。结果：I型错误通常在名义水平上。调整后的分析比未调整的分析具有更大的检测真正治疗效果的能力，尤其是在预先指定的且具有重大预测意义的策略下。协方差HR在2和5之间的样本量可能减少15％至44％（协变量患病率50％）和4％至12％（协变量患病率10％）。重大失衡战略产生了小幅减少。减少量越大，协变量效应越强，但与治疗效果，样本量和检查水平无关。结论：调整一项预测性基线特征比未进行调整的分析具有更大的检测真正治疗效果的能力，而不会增加I型错误，并且样本量可能会适度减少。具有事件发生时间结果的RCT的分析应针对预测协变量进行调整。

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