观察性研究中缺失数据的处理和报告框架：TARMOS框架
Framework for the Treatment And Reporting of Missing data in Observational Studies: The TARMOS framework
摘要：
缺失数据在医学研究中普遍存在。尽管关于如何处理缺失数据的指导越来越多，但实践变化缓慢，误解比比皆是，特别是在观察性研究中。我们提出了一个实用的框架来处理和报告观察性研究中不完整数据的分析，我们使用雅芳父母和孩子纵向研究的案例研究来说明。该框架包括三个步骤：1）制定分析计划，指定分析模型以及如何解决缺失数据。一个重要的考虑因素是完整的记录分析是否可能有效，多重插补或替代方法是否可能提供益处，以及是否需要关于缺失机制的敏感性分析。2)探索数据，检查分析计划中概述的方法是否适当，并进行预先计划的分析。3)报告结果，包括缺失数据的描述、如何解决缺失数据的详细信息，以及根据缺失数据和临床相关性解释的所有分析结果。该框架旨在支持研究人员系统地思考缺失数据，并透明地报告对研究结果的潜在影响。
英文摘要：
Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits, and whether a sensitivity analysis regarding the missingness mechanism is required. 2) Explore the data, checking the methods outlined in the analysis plan are appropriate, and conduct the pre-planned analysis. 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data, and transparently reporting the potential effect on the study results.
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