ABSTRACT We develop a Bayesian variable selection method for logistic regression models that can simultaneously accommodate qualitative covariates and interaction terms under various heredity constraints. We use expectation-maximization variable selection (EMVS) with a deterministic annealing variant as the platform for our method, due to its proven flexibility and efficiency. We propose a variance adjustment of the priors for the coefficients of qualitative covariates, which controls false-positive rates, and a flexible parameterization for interaction terms, which accommodates user-specified heredity constraints. This method can handle all pairwise interaction terms as well as a subset of specific interactions. Using simulation, we show that this method selects associated covariates better than the grouped LASSO and the LASSO with heredity constraints in various exploratory research scenarios encountered in epidemiological studies. We apply our method to identify genetic and non-genetic risk factors associated with smoking experimentation in a cohort of Mexican-heritage adolescents.

中文翻译：

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在具有相关协变量的逻辑回归模型中使用 EM 算法进行贝叶斯变量选择

摘要 我们为逻辑回归模型开发了一种贝叶斯变量选择方法，该方法可以同时适应各种遗传约束下的定性协变量和交互项。由于其经过验证的灵活性和效率，我们使用具有确定性退火变体的期望最大化变量选择 (EMVS) 作为我们方法的平台。我们建议对定性协变量系数的先验进行方差调整，以控制误报率，以及灵活的交互项参数化，以适应用户指定的遗传约束。该方法可以处理所有成对交互项以及特定交互的子集。使用模拟，我们表明，在流行病学研究中遇到的各种探索性研究场景中，该方法比分组 LASSO 和具有遗传约束的 LASSO 更好地选择相关协变量。我们应用我们的方法来确定与墨西哥裔青少年队列吸烟实验相关的遗传和非遗传风险因素。