Regularized approach for data missing not at random.
Regularized approach for data missing not at random.
复制标题
DOI：
10.1177/0962280217717760
复制
发表时间：
2019-01
影响因子：
2.3
通讯作者：
Chen YH
中科院分区：
文献类型：
--
作者：
Tseng CH;Chen YH
It is common in longitudinal studies that missing data occur due to subjects’ no response, missed visits, dropout, death or other reasons during the course of study. To perform valid analysis in this setting, data missing not at random (MNAR) have to be considered. However, models for data MNAR often suffer from the identifiability issue and hence result in difficulty in estimation and computational convergence. To ameliorate this issue, we propose the LASSO and Ridge regularized selection models that regularize the missing data mechanism model to handle data MNAR, with the regularization parameter selected via a cross validation procedure. The proposed models can be also employed for sensitivity analysis to examine the effects on inference of different assumptions about the missing data mechanism. We illustrate the performance of the proposed models via simulation studies and the analysis of data from a randomized clinical trial.
登录
查看更多内容
影响因子：
1.9
作者：
Chen, QX;Ibrahim, JG
通讯作者：
Ibrahim, JG
DOI：
10.1111/j.1467-9868.2007.00640.x
发表时间：
2008-01-01
影响因子：
5.8
作者：
Molenberghs, Geert;Beunckens, Caroline;Kenward, Michael G.
通讯作者：
Kenward, Michael G.
DOI：
10.1111/j.1467-985x.2004.00349.x
发表时间：
2005-01-01
影响因子：
2
作者：
Greenland, S
通讯作者：
Greenland, S
影响因子：
2.7
作者：
FIRTH, D
通讯作者：
FIRTH, D
影响因子：
158.5
作者：
Tashkin, Donald P.;Elashoff, Robert;Metersky, Mark
通讯作者：
Metersky, Mark