110588NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia.
Stat Methods Med Res. 2022 Dec;31(12):2456-2469. doi: 10.1177/09622802221122445. Epub 2022 Sep 6.
When a clinical trial stops early for benefit, the maximum likelihood estimate (MLE) of the treatment effect may be subject to overestimation bias. Several authors have proposed adjusting for this bias using the conditional MLE, which is obtained by conditioning on early stopping. However, this approach has a fundamental problem in that the adjusted estimate may not be in the direction of benefit, even though the study has stopped early due to benefit. In this paper, we address this problem by embedding both the MLE and the conditional MLE within a broader class of penalised likelihood estimates, and choosing a member of the class that is a favourable compromise between the two. This penalised MLE, and its associated confidence interval, always lie in the direction of benefit when the study stops early for benefit. We study its properties using both simulations and analyses of the ENZAMET trial in metastatic prostate cancer. Conditional on stopping early for benefit, the method is found to have good unbiasedness and coverage properties, along with very favourable efficiency at earlier interim analyses. We recommend the penalised MLE as a supplementary analysis to a conventional primary analysis when a clinical trial stops early for benefit.
当临床试验因疗效提前终止时，治疗效果的最大似然估计（MLE）可能存在高估偏差。一些作者已经提出通过条件 MLE 来调整这种偏差，条件 MLE 是通过对提前终止进行条件化得到的。然而，这种方法存在一个根本问题，即即使研究因疗效提前终止，调整后的估计值也可能不在受益的方向上。在本文中，我们通过将 MLE 和条件 MLE 嵌入到更广泛的惩罚似然估计类中，并选择在两者之间折衷的类成员来解决这个问题。当研究因疗效提前终止时，这种惩罚 MLE 及其相关置信区间总是朝着受益的方向。我们使用模拟和转移性前列腺癌的 ENZAMET 试验分析来研究其性质。在提前因疗效终止的条件下，该方法被发现具有良好的无偏性和覆盖性质，以及在早期中期分析中非常有利的效率。当临床试验因疗效提前终止时，我们建议将惩罚 MLE 作为常规主要分析的补充分析。
