Wang Jing
The University of Texas at Arlington, Arlington, TX, 76019, USA.
Lifetime Data Anal. 2019 Jul;25(3):469-479. doi: 10.1007/s10985-019-09469-x. Epub 2019 Apr 10.
Multivariate frailty models have been used for clustered survival data to characterize the relationship between the hazard of correlated failures/events and exposure variables and covariates. However, these models can introduce serious biases of the estimation for failures from complex surveys that may depend on the sampling design (informative or noninformative). In order to consistently estimate parameters, this paper considers weighting the multivariate frailty model by the inverse of the probability of selection at each stage of sampling. This follows the principle of the pseudolikelihood approach. The estimation is carried out by maximizing the penalized partial and marginal pseudolikelihood functions. The performance of the proposed estimator is assessed through a Monte Carlo simulation study and the 4 waves of data from the 1998-1999 Early Childhood Longitudinal Study. Results show that the weighted estimator is consistent and approximately unbiased.
多变量脆弱性模型已被用于聚类生存数据，以描述相关失败/事件的风险与暴露变量及协变量之间的关系。然而，这些模型可能会给来自复杂调查的失败估计带来严重偏差，这种偏差可能取决于抽样设计（信息性或非信息性）。为了一致地估计参数，本文考虑在抽样的每个阶段通过选择概率的倒数对多变量脆弱性模型进行加权。这遵循了伪似然方法的原则。估计是通过最大化惩罚后的部分和边际伪似然函数来进行的。通过蒙特卡罗模拟研究以及1998 - 1999年儿童早期纵向研究的4波数据评估了所提出估计量的性能。结果表明，加权估计量是一致的且近似无偏的。
