Adaptive Testing of Conditional Association Through Recursive Mixture Modeling
Adaptive Testing of Conditional Association Through Recursive Mixture Modeling
复制标题
通过递归混合建模进行条件关联的自适应测试
DOI：
10.1080/01621459.2013.838899
复制
发表时间：
2013
影响因子：
3.7
通讯作者：
Li Ma
中科院分区：
文献类型：
--
作者：
Li Ma
在许多病例对照研究中，中心目标是测试预测因子和响应之间的关联或依赖性。必须以相关协变量为条件，以避免误报和功效损失。在逻辑回归等参数框架中，通过将协变量作为附加变量合并到模型中，对协变量进行调节很容易。相比之下，非参数方法（例如 Cochran-Mantel-Haenszel 检验）通过将数据划分为多个层来完成调节，每个层对应一个可能的协变量值。在现代应用中，这通常会产生大量层，由于协变量和/或预测变量空间的多维性，其中大多数层都是稀疏的，而实际上，协变量空间通常仅由少量具有差分响应预测变量依赖性的子集组成。我们引入贝叶斯方法来从数据中推断出有效的分层并相应地测试关联性。我们框架的核心是预测变量的回顾分布的递归混合模型，其混合分布是协变量空间上的分区的先验。模型下的推理可以通过一系列递归以封闭形式高效进行，在模型灵活性和计算易处理性之间取得平衡。模拟研究表明，我们的方法在各种场景下都远远优于经典测试。本文的补充材料可在线获取。
In many case-control studies, a central goal is to test for association or dependence between the predictors and the response. Relevant covariates must be conditioned on to avoid false positives and loss in power. Conditioning on covariates is easy in parametric frameworks such as the logistic regression—by incorporating the covariates into the model as additional variables. In contrast, nonparametric methods such as the Cochran-Mantel-Haenszel test accomplish conditioning by dividing the data into strata, one for each possible covariate value. In modern applications, this often gives rise to numerous strata, most of which are sparse due to the multidimensionality of the covariate and/or predictor space, while in reality, the covariate space often consists of just a small number of subsets with differential response-predictor dependence. We introduce a Bayesian approach to inferring from the data such an effective stratification and testing for association accordingly. The core of our framework is a recursive mixture model on the retrospective distribution of the predictors, whose mixing distribution is a prior on the partitions on the covariate space. Inference under the model can proceed efficiently in closed form through a sequence of recursions, striking a balance between model flexibility and computational tractability. Simulation studies show that our method substantially outperforms classical tests under various scenarios. Supplementary materials for this article are available online.