Sinha Sanjoy K, Kaushal Amit, Xiao Wenzhong
School of Mathematics and Statistics, Carleton University, Ottawa, ON, K1S 5B6, Canada.
Stanford Genome Technology Center, Stanford, CA 94305, USA.
Comput Stat Data Anal. 2014 Apr;72:77-91. doi: 10.1016/j.csda.2013.10.027.
For the analysis of longitudinal data with nonignorable and nonmonotone missing responses, a full likelihood method often requires intensive computation, especially when there are many follow-up times. The authors propose and explore a Monte Carlo method, based on importance sampling, for approximating the maximum likelihood estimators. The finite-sample properties of the proposed estimators are studied using simulations. An application of the proposed method is also provided using longitudinal data on peptide intensities obtained from a proteomics experiment of trauma patients.
对于具有不可忽略且非单调缺失响应的纵向数据进行分析时，全似然方法通常需要大量计算，尤其是当有许多随访时间点时。作者提出并探索了一种基于重要性抽样的蒙特卡罗方法，用于近似最大似然估计量。通过模拟研究了所提出估计量的有限样本性质。还使用从创伤患者蛋白质组学实验获得的肽强度纵向数据给出了该方法的一个应用实例。
