47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Late RIA, R&D BioPharmaceuticals AstraZeneca, Gothenburg, Sweden.
Stat Methods Med Res. 2021 Mar;30(3):702-716. doi: 10.1177/0962280220970986. Epub 2020 Nov 24.
Composite endpoints that combine multiple outcomes on different scales are common in clinical trials, particularly in chronic conditions. In many of these cases, patients will have to cross a predefined responder threshold in each of the outcomes to be classed as a responder overall. One instance of this occurs in systemic lupus erythematosus, where the responder endpoint combines two continuous, one ordinal and one binary measure. The overall binary responder endpoint is typically analysed using logistic regression, resulting in a substantial loss of information. We propose a latent variable model for the systemic lupus erythematosus endpoint, which assumes that the discrete outcomes are manifestations of latent continuous measures and can proceed to jointly model the components of the composite. We perform a simulation study and find that the method offers large efficiency gains over the standard analysis, the magnitude of which is highly dependent on the components driving response. Bias is introduced when joint normality assumptions are not satisfied, which we correct for using a bootstrap procedure. The method is applied to the Phase IIb MUSE trial in patients with moderate to severe systemic lupus erythematosus. We show that it estimates the treatment effect 2.5 times more precisely, offering a 60% reduction in required sample size.
复合终点是将不同尺度的多个结果结合在一起的，在临床试验中很常见，特别是在慢性疾病中。在许多情况下，患者必须在每个结果中达到预先定义的应答者阈值，才能被总体归类为应答者。系统性红斑狼疮就是一个例子，其中的应答终点结合了两个连续、一个有序和一个二分类测量。通常使用逻辑回归分析总体二进制应答终点，这会导致大量信息丢失。我们提出了一个系统性红斑狼疮终点的潜在变量模型，该模型假设离散结果是潜在连续测量的表现，可以继续联合建模复合的组成部分。我们进行了一项模拟研究，发现该方法相对于标准分析提供了很大的效率增益，增益的幅度高度取决于驱动应答的组成部分。当联合正态性假设不满足时会引入偏差，我们使用自举程序进行了校正。该方法应用于中度至重度系统性红斑狼疮患者的 IIb 期 MUSE 试验。我们表明，它可以更精确地估计治疗效果，将所需的样本量减少 60%。
