Choi Jaeun, Zeng Donglin, Olshan Andrew F, Cai Jianwen
Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, New York, NY, 10461, USA.
Department of Biostatistics, University of North Carolina at Chapel Hill, McGavran-Greenberg Hl, 135 Dauer Drive, CB 7420, Chapel Hill, NC, 27599, USA.
Lifetime Data Anal. 2018 Jan;24(1):126-152. doi: 10.1007/s10985-017-9405-4. Epub 2017 Aug 30.
Joint models with shared Gaussian random effects have been conventionally used in analysis of longitudinal outcome and survival endpoint in biomedical or public health research. However, misspecifying the normality assumption of random effects can lead to serious bias in parameter estimation and future prediction. In this paper, we study joint models of general longitudinal outcomes and survival endpoint but allow the underlying distribution of shared random effect to be completely unknown. For inference, we propose to use a mixture of Gaussian distributions as an approximation to this unknown distribution and adopt an Expectation-Maximization (EM) algorithm for computation. Either AIC and BIC criteria are adopted for selecting the number of mixtures. We demonstrate the proposed method via a number of simulation studies. We illustrate our approach with the data from the Carolina Head and Neck Cancer Study (CHANCE).
具有共享高斯随机效应的联合模型传统上用于生物医学或公共卫生研究中的纵向结果和生存终点分析。然而，错误指定随机效应的正态性假设可能会导致参数估计和未来预测出现严重偏差。在本文中，我们研究了一般纵向结果和生存终点的联合模型，但允许共享随机效应的潜在分布完全未知。为了进行推断，我们建议使用高斯分布的混合来近似这个未知分布，并采用期望最大化（EM）算法进行计算。采用AIC和BIC准则来选择混合的数量。我们通过大量模拟研究证明了所提出的方法。我们用卡罗来纳头颈癌研究（CHANCE）的数据说明了我们的方法。
