Institute for Measurement, Methodology, Analysis, and Policy, Texas Tech University, Lubbock, USA.
Prev Sci. 2018 Apr;19(3):284-294. doi: 10.1007/s11121-016-0644-5.
We review a number of issues regarding missing data treatments for intervention and prevention researchers. Many of the common missing data practices in prevention research are still, unfortunately, ill-advised (e.g., use of listwise and pairwise deletion, insufficient use of auxiliary variables). Our goal is to promote better practice in the handling of missing data. We review the current state of missing data methodology and recent missing data reporting in prevention research. We describe antiquated, ad hoc missing data treatments and discuss their limitations. We discuss two modern, principled missing data treatments: multiple imputation and full information maximum likelihood, and we offer practical tips on how to best employ these methods in prevention research. The principled missing data treatments that we discuss are couched in terms of how they improve causal and statistical inference in the prevention sciences. Our recommendations are firmly grounded in missing data theory and well-validated statistical principles for handling the missing data issues that are ubiquitous in biosocial and prevention research. We augment our broad survey of missing data analysis with references to more exhaustive resources.
我们回顾了一些关于干预和预防研究中缺失数据处理的问题。不幸的是，许多预防研究中常见的缺失数据实践仍然是不明智的（例如，使用完全删除和成对删除，辅助变量的使用不足）。我们的目标是促进缺失数据处理方面的更好实践。我们回顾了缺失数据方法的现状和预防研究中最近的缺失数据报告。我们描述了过时的、特定于问题的缺失数据处理方法，并讨论了它们的局限性。我们讨论了两种现代的、有原则的缺失数据处理方法：多重插补和完全信息最大似然法，并提供了在预防研究中如何最好地使用这些方法的实用技巧。我们讨论的有原则的缺失数据处理方法是基于它们如何改善预防科学中的因果推断和统计推断。我们的建议是基于缺失数据理论和处理生物社会和预防研究中普遍存在的缺失数据问题的经过良好验证的统计原则。我们通过引用更详尽的资源来扩充我们对缺失数据分析的广泛调查。
