Inference for longitudinal data with nonignorable nonmonotone missing responses

被引：3
|
作者：
Sinha, Sanjoy K. [1 ]
Kaushal, Amit [2 ]
Xiao, Wenzhong [3 ]
机构：
[1] Carleton Univ, Sch Math & Stat, Ottawa, ON K1S 5B6, Canada
[2] Stanford Genome Technol Ctr, Stanford, CA 94305 USA
[3] Harvard Univ, Sch Med, Boston, MA 02114 USA
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2014年 / 72卷
基金：
加拿大自然科学与工程研究理事会;
关键词：
False discovery rate; Importance sampling; Incomplete data; Linear mixed model; Longitudinal study; Maximum likelihood; Proteomics experiment; SENSITIVITY-ANALYSIS; DATA MECHANISM; MIXED MODELS; DROP-OUT; REGRESSION; NONRESPONSE;
D O I：
10.1016/j.csda.2013.10.027
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：77 / 91
页数：15
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