Nonparametric Bayes modeling for case control studies with many predictors.
Nonparametric Bayes modeling for case control studies with many predictors.
复制标题
用于具有许多预测变量的病例对照研究的非参数贝叶斯建模。
DOI：
10.1111/biom.12411
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发表时间：
2016
期刊：
影响因子：
1.9
通讯作者：
NationalBirthDefectsPreventionStudy
中科院分区：
文献类型：
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作者：
Zhou,Jing;Herring,AmyH;Bhattacharya,Anirban;Olshan,AndrewF;Dunson,DavidB;NationalBirthDefectsPreventionStudy
在生物医学研究中，进行涉及高维预测因子的病例对照研究是很常见的，主要目标是检测与疾病有显著相关性的预测因子的稀疏子集。回归分析依赖于独立的筛选，一次考虑一个预测因子，或者在某些情况下依赖于逻辑回归，假设没有相互作用。我们提出了一个根本不同的方法，基于非参数贝叶斯低秩张量因子分解模型的回顾性可能性。我们的模型允许一个非常灵活的结构，在表征多元变量的分布为未知的，没有任何线性假设，在逻辑回归。只有当预测因子对疾病风险没有直接影响或通过与其他预测因子的相互作用而产生影响时，它们才被排除在外。因此，我们获得了一个综合的方法来筛选重要的预测因子。计算依赖于有效的吉布斯采样器。该方法被证明具有高功率和低错误发现率在模拟研究中，我们认为应用到出生缺陷的流行病学研究。
It is common in biomedical research to run case-control studies involving high-dimensional predictors, with the main goal being detection of the sparse subset of predictors having a significant association with disease. Usual analyses rely on independent screening, considering each predictor one at a time, or in some cases on logistic regression assuming no interactions. We propose a fundamentally different approach based on a nonparametric Bayesian low rank tensor factorization model for the retrospective likelihood. Our model allows a very flexible structure in characterizing the distribution of multivariate variables as unknown and without any linear assumptions as in logistic regression. Predictors are excluded only if they have no impact on disease risk, either directly or through interactions with other predictors. Hence, we obtain an omnibus approach for screening for important predictors. Computation relies on an efficient Gibbs sampler. The methods are shown to have high power and low false discovery rates in simulation studies, and we consider an application to an epidemiology study of birth defects.