Incomplete hierarchical dat
Incomplete hierarchical dat
复制标题
DOI：
10.1177/0962280206075310
复制
发表时间：
2007-01-01
影响因子：
2.3
通讯作者：
Verbeke, Geert
中科院分区：
文献类型：
--
作者：
Beunckens, Caroline;Molenberghs, Geert;Verbeke, Geert
研究人员在收集分层数据时，经常会遇到数据的不完备性问题。由于控制失配的过程通常不在研究人员的控制范围内，无论实验设计得有多好，在分析这些数据时都需要仔细注意。我们在很大程度上基于Rubin的工作绘制了一个标准框架和分类法。在简要介绍了(过于)简单的方法之后，我们转向一些可行的标准分析候选方法，包括直接似然法、多重归因法和广义估计方程的版本。其中许多都需要所谓的无知。在后一个条件不一定满足的情况下，我们还同时回顾了结果和缺失过程的灵活模型。最后，我们说明了这些方法是如何对建模假设非常敏感的，然后总结了一些用于敏感性分析的路线。将注意拟议的分析模式在监管环境中的可行性。
The researcher collecting hierarchical data is frequently confronted with incompleteness. Since the processes governing missingness are often outside the investigator's control, no matter how well the experiment has been designed, careful attention is needed when analyzing such data. We sketch a standard framework and taxonomy largely based on Rubin's work. After briefly touching upon (overly) simple methods, we turn to a number of viable candidates for a standard analysis, including direct likelihood, multiple imputation and versions of generalized estimating equations. Many of these require so-called ignorability. With the latter condition not necessarily satisfied, we also review flexible models for the outcome and missingness processes at the same time. Finally, we illustrate how such methods can be very sensitive to modeling assumptions and then conclude with a number of routes for sensitivity analysis. Attention will be given to the feasibility of the proposed modes of analysis within a regulatory environment.