Motivated by a genome-wide association study to discover risk variants for the progression of Age-related Macular Degeneration (AMD), we develop a computationally efficient copula-based score test, in which the dependence between bivariate progression times is taken into account. Specifically, a two-step estimation approach with numerical derivatives to approximate the score function and observed information matrix is proposed. Both parametric and weakly parametric marginal distributions under the proportional hazards assumption are considered. Extensive simulation studies are conducted to evaluate the Type I error control and power performance of the proposed method. Finally, we apply our method to a large randomized trial data, the Age-related Eye Disease Study, to identify susceptible risk variants for AMD progression. The top variants identified on Chromosome 10 show significantly differential progression profiles for different genetic groups, which are critical in characterizing and predicting the risk of progression-to-late-AMD for patients with mild to moderate AMD.

中文翻译：

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基于Copula的双变量时间事件数据评分测试，应用于AMD进展的遗传研究。

受全基因组关联研究的启发，发现与年龄相关的黄斑变性（AMD）进展的风险变异，我们开发了一种计算有效的基于copula的评分测试，其中考虑了双变量进展时间之间的依赖性。具体而言，提出了一种采用数值导数逼近得分函数和观测信息矩阵的两步估计方法。考虑比例风险假设下的参数和弱参数边际分布。进行了广泛的仿真研究，以评估所提出方法的I型错误控制和功率性能。最后，我们将我们的方法应用于大量的随机试验数据（与年龄有关的眼疾研究）中，以鉴定AMD进展的易感风险变异。