Cai Jianwen, Zeng Donglin
Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina 27599-7420, USA.
Biometrics. 2011 Dec;67(4):1340-51. doi: 10.1111/j.1541-0420.2011.01590.x. Epub 2011 Mar 18.
We propose an additive mixed effect model to analyze clustered failure time data. The proposed model assumes an additive structure and includes a random effect as an additional component. Our model imitates the commonly used mixed effect models in repeated measurement analysis but under the context of hazards regression; our model can also be considered as a parallel development of the gamma-frailty model in additive model structures. We develop estimating equations for parameter estimation and propose a way of assessing the distribution of the latent random effect in the presence of large clusters. We establish the asymptotic properties of the proposed estimator. The small sample performance of our method is demonstrated via a large number of simulation studies. Finally, we apply the proposed model to analyze data from a diabetic study and a treatment trial for congestive heart failure.
我们提出了一种加法混合效应模型来分析聚类失效时间数据。所提出的模型假定具有加法结构，并将随机效应作为一个附加成分包含在内。我们的模型模仿了重复测量分析中常用的混合效应模型，但处于风险回归的背景下；我们的模型也可被视为加法模型结构中伽马脆弱模型的平行发展。我们开发了用于参数估计的估计方程，并提出了一种在存在大聚类的情况下评估潜在随机效应分布的方法。我们建立了所提出估计量的渐近性质。通过大量模拟研究展示了我们方法的小样本性能。最后，我们应用所提出的模型来分析来自一项糖尿病研究和一项充血性心力衰竭治疗试验的数据。
