Simultaneous variable selection for joint models of longitudinal and survival outcomes.
Simultaneous variable selection for joint models of longitudinal and survival outcomes.
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作者：
He Z;Tu W;Wang S;Fu H;Yu Z
纵向和生存结局的联合模型在临床研究中的使用频率越来越高。固定效应和随机效应的正确规范对于实际数据分析至关重要。同时选择纵向和生存分量中的变量是防止模型误设的必要保障。然而，在这样的模型中的变量选择还没有被研究。据我们所知，没有现有的计算工具可供从业人员使用。在本文中，我们描述了一个惩罚似然方法与自适应最小绝对收缩和选择算子（ALASSO）的惩罚函数，同时选择联合模型中的固定和随机效应。为了在随机效应的方差分量中进行选择，我们使用Cholesky分解重新参数化方差分量;在这样做时，引入了组收缩的惩罚函数。为了减少惩罚导致的估计偏差，我们提出了一个两阶段的选择过程中，偏差的大小在第二阶段得到改善。惩罚似然近似高斯求积和EM算法优化。仿真研究表明，第一阶段的选择效果很好，第二阶段的估计偏差很小。为了说明，我们分析了一组心力衰竭患者中纵向观察到的临床标志物和患者生存率。
Joint models of longitudinal and survival outcomes have been used with increasing frequency in clinical investigations. Correct specification of fixed and random effects is essential for practical data analysis. Simultaneous selection of variables in both longitudinal and survival components functions as a necessary safeguard against model misspecification. However, variable selection in such models has not been studied. No existing computational tools, to the best of our knowledge, have been made available to practitioners. In this paper, we describe a penalized likelihood method with adaptive least absolute shrinkage and selection operator (ALASSO) penalty functions for simultaneous selection of fixed and random effects in joint models. To perform selection in variance components of random effects, we reparameterize the variance components using a Cholesky decomposition; in doing so, a penalty function of group shrinkage is introduced. To reduce the estimation bias resulted from penalization, we propose a two-stage selection procedure in which the magnitude of the bias is ameliorated in the second stage. The penalized likelihood is approximated by Gaussian quadrature and optimized by an EM algorithm. Simulation study showed excellent selection results in the first stage and small estimation biases in the second stage. To illustrate, we analyzed a longitudinally observed clinical marker and patient survival in a cohort of patients with heart failure.