Xu Ronghui, Gamst Anthony
Division of Biostatistics and Bioinformatics, Department of Family and Preventive Medicine, University of California, San Diego, CA 92093-0112, USA.
Lifetime Data Anal. 2007 Sep;13(3):317-32. doi: 10.1007/s10985-007-9041-5. Epub 2007 Jul 19.
The proportional hazards mixed-effects model (PHMM) was proposed to handle dependent survival data. Motivated by its application in genetic epidemiology, we study the interpretation of its parameter estimates under violations of the proportional hazards assumption. The estimated fixed effect turns out to be an averaged regression effect over time, while the estimated variance component could be unaffected, inflated or attenuated depending on whether the random effect is on the baseline hazard, and whether the non-proportional regression effect decreases or increases over time. Using the conditional distribution of the covariates we define the standardized covariate residuals, which can be used to check the proportional hazards assumption. The model checking technique is illustrated on a multi-center lung cancer trial.
提出比例风险混合效应模型（PHMM）来处理相依生存数据。受其在遗传流行病学中应用的启发，我们研究了在违反比例风险假设的情况下其参数估计的解释。结果表明，估计的固定效应是随时间平均的回归效应，而估计的方差分量可能不受影响、膨胀或衰减，这取决于随机效应是否作用于基线风险，以及非比例回归效应随时间是减小还是增大。利用协变量的条件分布，我们定义了标准化协变量残差，可用于检验比例风险假设。在一项多中心肺癌试验中展示了模型检验技术。
