Identification of homogeneous and heterogeneous variables in pooled cohort studies.
Identification of homogeneous and heterogeneous variables in pooled cohort studies.
复制标题
在合并队列研究中鉴定均质和异质变量。
作者：
Cheng X;Lu W;Liu M
合并分析整合了来自多个研究的数据，并实现了更大的样本量，以增强统计能力。当变量对研究结果的影响存在异质性时，简单的汇总策略无法公平完整地描述异质性变量的影响。因此，在合并研究中研究变量的同质和异质结构是很重要的。在本文中，我们考虑了具有事件时间结果的合并队列研究，并提出了一种惩罚Cox偏似然方法，该方法对变量的同质和异质效应进行了自适应加权复合惩罚。我们表明，我们的方法可以将变量表征为具有异质，均匀或零效应，并估计非零效应。结果很容易推广到高维应用，其中参数的数量大于样本量。所提出的选择和估计过程可以使用迭代射击算法来实现。我们进行了大量的数值研究来评估我们提出的方法的性能，并通过对卵巢癌患者基因表达的汇总分析来证明它。
Pooled analyses integrate data from multiple studies and achieve a larger sample size for enhanced statistical power. When heterogeneity exists in variables’ effects on the outcome across studies, the simple pooling strategy fails to present a fair and complete picture of the effects of heterogeneous variables. Thus, it is important to investigate the homogeneous and heterogeneous structure of variables in pooled studies. In this paper, we consider the pooled cohort studies with time-to-event outcomes and propose a penalized Cox partial likelihood approach with adaptively weighted composite penalties on variables’ homogeneous and heterogeneous effects. We show that our method can characterize the variables as having heterogeneous, homogeneous, or null effects, and estimate non-zero effects. The results are readily extended to high-dimensional applications where the number of parameters is larger than the sample size. The proposed selection and estimation procedure can be implemented using the iterative shooting algorithm. We conduct extensive numerical studies to evaluate the performance of our proposed method and demonstrate it using a pooled analysis of gene expression in patients with ovarian cancer.