1 Institute of public health, College of Medicine & Health Sciences, United Arab Emirates University (UAEU), United Arab Emirates.
2 Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université catholique de Louvain, Louvain-la-Neuve, Belgium.
Stat Methods Med Res. 2019 Jan;28(1):151-169. doi: 10.1177/0962280217717761. Epub 2017 Jul 3.
Composite endpoints are frequently used in clinical outcome trials to provide more endpoints, thereby increasing statistical power. A key requirement for a composite endpoint to be meaningful is the absence of the so-called qualitative heterogeneity to ensure a valid overall interpretation of any treatment effect identified. Qualitative heterogeneity occurs when individual components of a composite endpoint exhibit differences in the direction of a treatment effect. In this paper, we develop a general statistical method to test for qualitative heterogeneity, that is to test whether a given set of parameters share the same sign. This method is based on the intersection-union principle and, provided that the sample size is large, is valid whatever the model used for parameters estimation. We propose two versions of our testing procedure, one based on a random sampling from a Gaussian distribution and another version based on bootstrapping. Our work covers both the case of completely observed data and the case where some observations are censored which is an important issue in many clinical trials. We evaluated the size and power of our proposed tests by carrying out some extensive Monte Carlo simulations in the case of multivariate time to event data. The simulations were designed under a variety of conditions on dimensionality, censoring rate, sample size and correlation structure. Our testing procedure showed very good performances in terms of statistical power and type I error. The proposed test was applied to a data set from a single-center, randomized, double-blind controlled trial in the area of Alzheimer's disease.
复合终点通常用于临床结局试验中，以提供更多的终点，从而提高统计效力。复合终点有意义的一个关键要求是不存在所谓的定性异质性，以确保对任何确定的治疗效果进行有效综合解释。当复合终点的各个组成部分在治疗效果的方向上表现出差异时，就会出现定性异质性。本文提出了一种用于检验定性异质性的一般统计方法，即检验一组给定的参数是否具有相同的符号。该方法基于交集并集原理，只要样本量足够大，无论用于参数估计的模型如何，都是有效的。我们提出了两种版本的检验程序，一种基于从正态分布中随机抽样，另一种基于自举法。我们的工作涵盖了完全观测数据的情况和部分观测数据被删失的情况，这在许多临床试验中是一个重要问题。我们在多维生存数据的情况下进行了广泛的蒙特卡罗模拟，以评估我们提出的检验的功效和检验效能。模拟设计了多种条件，包括维度、删失率、样本量和相关结构。我们的检验程序在统计功效和Ⅰ类错误方面表现出了非常好的性能。该检验程序应用于来自阿尔茨海默病单中心、随机、双盲对照试验的数据。
