Gallardo Diego I, Bourguignon Marcelo, Santibáñez John L
Departamento de Estadística, Facultad de Ciencias, Universidad del Bío-Bío, Concepción, Chile.
Department of Statistics, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.
Biom J. 2025 Apr;67(2):e70044. doi: 10.1002/bimj.70044.
The primary goal of this paper is to introduce a novel frailty model based on the weighted Lindley (WL) distribution for modeling clustered survival data. We study the statistical properties of the proposed model. In particular, the amount of unobserved heterogeneity is directly parameterized by the variance of the frailty distribution such as gamma and inverse Gaussian frailty models. Parametric and semiparametric versions of the WL frailty model are studied. A simple expectation-maximization (EM) algorithm is proposed for parameter estimation. Simulation studies are conducted to evaluate its finite sample performance. Finally, we apply the proposed model to a real data set to analyze times after surgery in patients diagnosed with infiltrating ductal carcinoma and compare our results with classical frailty models carried out in this application, which shows the superiority of the proposed model. We implement an R package that includes estimation for fitting the proposed model based on the EM algorithm.
本文的主要目标是引入一种基于加权林德利（WL）分布的新型脆弱模型，用于对聚类生存数据进行建模。我们研究了所提出模型的统计特性。特别是，未观察到的异质性数量直接由脆弱分布的方差参数化，如伽马和逆高斯脆弱模型。研究了WL脆弱模型的参数化和半参数化版本。提出了一种简单的期望最大化（EM）算法用于参数估计。进行了模拟研究以评估其有限样本性能。最后，我们将所提出的模型应用于一个真实数据集，以分析浸润性导管癌患者术后的时间，并将我们的结果与在此应用中使用的经典脆弱模型进行比较，这显示了所提出模型的优越性。我们实现了一个R包，其中包括基于EM算法对所提出模型进行拟合的估计。
