arXiv 1902.070372019-02-19stat.ME

应用潜变量模型提高复合终点分析的效率

Employing latent variable models to improve efficiency in composite endpoint analysis

Martina McMenamin, Jessica K. Barrett, Anna Berglind, James M. S. Wason

备注：: 44 pages, 12 figures

摘要：

在临床试验中，特别是在慢性疾病中，结合不同尺度上的多种结局的复合终点很常见。在许多情况下，患者必须在每个结局中超过预定义的应答者阈值，才能被归类为总体应答者。这种情况发生在系统性红斑狼疮（SLE）中，其中应答者终点结合了两个连续的，一个序数和一个二元测量。通常使用逻辑斯谛回归分析总体二元应答者终点，导致大量信息丢失。我们提出了一个潜在的变量模型的SLE终点，它假设离散的结果是潜在的连续措施的表现，并可以继续联合建模的组成部分的复合。我们进行了模拟研究，并找到了方法，以提供大的效率增益超过标准的分析。我们发现，精度增益的大小高度依赖于哪些组件是驱动响应。当联合正态性假设不满足时，引入偏差，我们使用自助程序进行校正。该方法适用于IIb期MUSE试验中的中度至重度SLE患者。我们表明，它估计的处理效应2.5倍更精确，提供了60%的减少所需的样本量。

英文摘要：

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 (SLE), 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 SLE 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 the method to offer large efficiency gains over the standard analysis. We find that the magnitude of the precision gains are highly dependent on which components are 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 SLE. We show that it estimates the treatment effect 2.5 times more precisely, offering a 60% reduction in required sample size.

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