Department of Psychology, Arizona State University,Tempe, AZ 85287–1104, USA.
Psychol Methods. 2011 Mar;16(1):1-16. doi: 10.1037/a0022640.
The past decade has seen a noticeable shift in missing data handling techniques that assume a missing at random (MAR) mechanism, where the propensity for missing data on an outcome is related to other analysis variables. Although MAR is often reasonable, there are situations where this assumption is unlikely to hold, leading to biased parameter estimates. One such example is a longitudinal study of substance use where participants with the highest frequency of use also have the highest likelihood of attrition, even after controlling for other correlates of missingness. There is a large body of literature on missing not at random (MNAR) analysis models for longitudinal data, particularly in the field of biostatistics. Because these methods allow for a relationship between the outcome variable and the propensity for missing data, they require a weaker assumption about the missing data mechanism. This article describes 2 classic MNAR modeling approaches for longitudinal data: the selection model and the pattern mixture model. To date, these models have been slow to migrate to the social sciences, in part because they required complicated custom computer programs. These models are now quite easy to estimate in popular structural equation modeling programs, particularly Mplus. The purpose of this article is to describe these MNAR modeling frameworks and to illustrate their application on a real data set. Despite their potential advantages, MNAR-based analyses are not without problems and also rely on untestable assumptions. This article offers practical advice for implementing and choosing among different longitudinal models.
过去十年中，人们对缺失数据处理技术的看法发生了明显的转变，这些技术假设缺失数据的机制是随机缺失（MAR），即数据缺失的倾向与其他分析变量有关。虽然 MAR 通常是合理的，但在某些情况下，这种假设不太可能成立，从而导致参数估计出现偏差。例如，在一项关于物质使用的纵向研究中，即使在控制了其他缺失相关因素后，使用频率最高的参与者也最有可能流失。在纵向数据的缺失非随机（MNAR）分析模型方面，有大量的文献，特别是在生物统计学领域。由于这些方法允许结局变量与缺失数据的倾向之间存在关系，因此它们对缺失数据机制的假设较弱。本文描述了两种用于纵向数据的经典 MNAR 建模方法：选择模型和模式混合模型。迄今为止，这些模型在社会科学领域的应用进展缓慢，部分原因是它们需要复杂的定制计算机程序。现在，这些模型在流行的结构方程建模程序（尤其是 Mplus）中非常容易估计。本文的目的是描述这些 MNAR 建模框架，并说明它们在真实数据集上的应用。尽管 MNAR 分析具有潜在的优势，但也并非没有问题，而且还依赖于未经检验的假设。本文提供了在不同纵向模型之间进行实施和选择的实用建议。
