Survival models allowing for random effects (e.g., frailty models) have been widely used for analyzing clustered time‐to‐event data. Accelerated failure time (AFT) models with random effects are useful alternatives to frailty models. Because survival times are directly modeled, interpretation of the fixed and random effects is straightforward. Moreover, the fixed effect estimates are robust against various violations of the assumed model. In this paper, we propose a penalized h‐likelihood (HL) procedure for variable selection of fixed effects in the AFT random‐effect models. For the purpose of variable selection, we consider three penalty functions, namely, least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), and HL. We demonstrate via simulation studies that the proposed variable selection procedure is robust against the misspecification of the assumed model. The proposed method is illustrated using data from a bladder cancer clinical trial.

中文翻译：

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AFT 随机效应模型中变量选择的惩罚 h 似然方法

允许随机效应的生存模型（例如，衰弱模型）已被广泛用于分析聚集的时间事件数据。具有随机效应的加速失效时间 (AFT) 模型是脆弱模型的有用替代方案。因为生存时间是直接建模的，所以对固定和随机效应的解释很简单。此外，固定效应估计对于假设模型的各种违规行为是稳健的。在本文中，我们提出了一种惩罚 h 似然 (HL) 程序，用于在 AFT 随机效应模型中选择固定效应的变量。出于变量选择的目的，我们考虑了三个惩罚函数，即最小绝对收缩和选择算子（LASSO）、平滑剪裁绝对偏差（SCAD）和 HL。我们通过模拟研究证明，所提出的变量选择程序对于假设模型的错误指定具有鲁棒性。使用来自膀胱癌临床试验的数据说明了所提出的方法。