1 Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
2 Statistical Methodology, Novartis Pharma AG, Basel, Switzerland.
Stat Methods Med Res. 2019 Aug;28(8):2385-2403. doi: 10.1177/0962280218780538. Epub 2018 Jun 11.
Robust semiparametric models for recurrent events have received increasing attention in the analysis of clinical trials in a variety of diseases including chronic heart failure. In comparison to parametric recurrent event models, robust semiparametric models are more flexible in that neither the baseline event rate nor the process inducing between-patient heterogeneity needs to be specified in terms of a specific parametric statistical model. However, implementing group sequential designs in the robust semiparametric model is complicated by the fact that the sequence of Wald statistics does not follow asymptotically the canonical joint distribution. In this manuscript, we propose two types of group sequential procedures for a robust semiparametric analysis of recurrent events. The first group sequential procedure is based on the asymptotic covariance of the sequence of Wald statistics and it guarantees asymptotic control of the type I error rate. The second procedure is based on the canonical joint distribution and does not guarantee asymptotic type I error rate control but is easy to implement and corresponds to the well-known standard approach for group sequential designs. Moreover, we describe how to determine the maximum information when planning a clinical trial with a group sequential design and a robust semiparametric analysis of recurrent events. We contrast the operating characteristics of the proposed group sequential procedures in a simulation study motivated by the ongoing phase 3 PARAGON-HF trial (ClinicalTrials.gov identifier: NCT01920711) in more than 4600 patients with chronic heart failure and a preserved ejection fraction. We found that both group sequential procedures have similar operating characteristics and that for some practically relevant scenarios, the group sequential procedure based on the canonical joint distribution has advantages with respect to the control of the type I error rate. The proposed method for calculating the maximum information results in appropriately powered trials for both procedures.
用于分析包括慢性心力衰竭在内的各种疾病临床试验的复发事件的稳健半参数模型受到越来越多的关注。与参数复发事件模型相比，稳健半参数模型更加灵活，因为不必根据特定的参数统计模型来指定基线事件率或导致患者间异质性的过程。然而，在稳健半参数模型中实施分组序贯设计会变得复杂，因为 Wald 统计量的序列并不遵循渐近正则联合分布。在本文中，我们提出了两种用于复发事件稳健半参数分析的分组序贯程序。第一种分组序贯程序基于 Wald 统计量序列的渐近协方差，可保证 I 型错误率的渐近控制。第二种程序基于正则联合分布，不能保证渐近 I 型错误率控制，但易于实施，对应于分组序贯设计的常用标准方法。此外，我们描述了如何在具有分组序贯设计和复发事件稳健半参数分析的临床试验规划中确定最大信息量。我们在一项针对 4600 多名慢性心力衰竭和射血分数保留患者的正在进行的 PARAGON-HF 试验（ClinicalTrials.gov 标识符：NCT01920711）的模拟研究中对比了这两种分组序贯程序的操作特征。我们发现，这两种分组序贯程序都具有相似的操作特征，并且对于一些实际相关的情况，基于正则联合分布的分组序贯程序在 I 型错误率控制方面具有优势。所提出的计算最大信息量的方法为这两种程序都得到了适当功效的试验。
