IMPLEMENTATION OF GROUP SEQUENTIAL LOGRANK TESTS IN A MAXIMUM DURATION TRIAL
IMPLEMENTATION OF GROUP SEQUENTIAL LOGRANK TESTS IN A MAXIMUM DURATION TRIAL
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DOI：
10.2307/2532094
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发表时间：
1990-09-01
期刊：
影响因子：
1.9
通讯作者：
LACHIN, JM
中科院分区：
文献类型：
--
作者：
LAN, KKG;LACHIN, JM
为了控制使用对数秩检验的成组序贯程序中的I类错误概率，重要的是要知道为数据监测目的进行中期分析时的信息时间（分数）。对于对数秩检验，中期分析的信息时间是整个试验中累积的事件总数的分数。在最大信息试验设计中，当累积了预先规定的事件总数时，试验结束。 因此，对于这种设计，每次中期分析的信息时间是已知的。然而，许多试验的目的是在一个固定的随访时间内对特定数量的患者积累数据。这被称为最长持续时间试验设计。在这种设计下，在中期分析时，累积的事件总数未知。因此，对于最长持续时间试验设计，需要估计这些信息时间。通常的做法是假设在任何两个连续的中期分析之间将累积固定的信息分数，然后使用Pocock或O '' Brien-Fleming边界。在这篇文章中，我们描述了一个基于总患者暴露分数的信息时间估计，这往往是轻微的负偏差（即，如果生存是指数分布的，则为保守的。然后，我们提出了一个数值探索的鲁棒性时，非指数生存的估计。我们还表明，用于构建具有所需I型错误控制水平的组顺序边界的Lan-DeMets（1983，Biometrika 70，659-663）程序可以使用估计的信息分数来计算，即使它可能有偏差。最后，我们讨论了采用有偏估计的研究信息组序贯程序的影响。
To control the Type I error probability in a group sequential procedure using the logrank test, it is important to know the information times (fractions) at the times of interim analyses conducted for purposes of data monitoring. For the logrank test, the information time at an interim analysis is the fraction of the total number of events to be accrued in the entire trial. In a maximum information trial design, the trial is concluded when a prespecified total number of events has been accrued. For such a design, therefore, the information time at each interim analysis is known. However, many trials are designed to accrue data over a fixed duration of follow-up on a specified number of patients. This is termed a maximum duration trial design. Under such a design, the total number of events to be accrued is unknown at the time of an interim analysis. For a maximum duration trial design, therefore, these information times need to be estimated. A common practice is to assume that a fixed fraction of information will be accrued between any two consecutive interim analyses, and then employ a Pocock or O''Brien-Fleming boundary. In this article, we describe an estimate of the information time based on the fraction of total patient exposure, which tends to be slightly negatively biased (i.e., conservative) if survival is exponentially distributed. We then present a numerical exploration of the robustness of this estimate when nonexponential survival applies. We also show that the Lan-DeMets (1983, Biometrika 70, 659-663) procedure for constructing group sequential boundaries with the desired level of Type I error control can be computed using the estimated information fraction, even though it may be biased. Finally, we discuss the implications of employing a biased estimate of study information for a group sequential procedure.