Bayesian accelerated failure time models based on penalized mixtures of Gaussians: regularization and variable selection
Bayesian accelerated failure time models based on penalized mixtures of Gaussians: regularization and variable selection
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DOI：
10.1007/s10182-014-0240-6
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发表时间：
2014-11
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影响因子：
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通讯作者：
Susanne Konrath;L. Fahrmeir;T. Kneib
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文献类型：
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作者：
Susanne Konrath;L. Fahrmeir;T. Kneib
在许多涉及持续时间分析的生物统计应用中，尤其是那些包含高维遗传信息的应用中，需要经典加速失效时间（AFT）模型的以下三个扩展：（1）生存时间分布的灵活的非参数估计，（二）结构化加性预测因子，包括连续协变量的线性和非线性效应，以及可能的其他类型的效应，例如随机或空间影响，以及（3）高维效应向量的正则化和变量选择。虽然大量的研究已经分别处理这些功能，AFT模型的发展，结合它们在一个统一的框架还没有被考虑。我们提出了一种贝叶斯方法，在这种灵活的AFT模型建模和推理，结合惩罚高斯混合误差分布，贝叶斯P-样条作为主要成分的结构化加性预测，贝叶斯版本的脊和LASSO以及穗和板先验，以执行稀疏。回归系数的先验是条件高斯的，便于马尔可夫链蒙特卡罗推断。所提出的模型类在模拟研究中进行了广泛的测试，并将其应用于考虑微阵列信息以及临床协变量作为预后因素的急性髓性白血病生存时间的分析。
In many biostatistical applications concerned with the analysis of duration times and especially those including high-dimensional genetic information, the following three extensions of classical accelerated failure time (AFT) models are required: (1) a flexible, nonparametric estimate of the survival time distribution, (2) a structured additive predictor including linear as well as nonlinear effects of continuous covariates and possibly further types of effects such as random or spatial effects, and (3) regularization and variable selection of high-dimensional effect vectors. Although a lot of research has dealt with these features separately, the development of AFT models combining them in a unified framework has not been considered yet. We present a Bayesian approach for modeling and inference in such flexible AFT models, incorporating a penalized Gaussian mixture error distribution, a structured additive predictor with Bayesian P-splines as a main ingredient, and Bayesian versions of ridge and LASSO as well as a spike and slab priors to enforce sparseness. Priors for regression coefficients are conditionally Gaussian, facilitating Markov chain Monte Carlo inference. The proposed model class is extensively tested in simulation studies and applied in the analysis of acute myeloid leukemia survival times considering microarray information as well as clinical covariates as prognostic factors.