Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework.
Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework.
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DOI：
10.1016/j.jclinepi.2021.01.008
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发表时间：
2021-06
影响因子：
7.2
通讯作者：
STRATOS initiative
中科院分区：
文献类型：
--
作者：
Lee KJ;Tilling KM;Cornish RP;Little RJA;Bell ML;Goetghebeur E;Hogan JW;Carpenter JR;STRATOS initiative
缺失数据在医学研究中无处不在。尽管关于如何处理缺失数据的指导越来越多，但实践正在缓慢变化，误解比比皆是，尤其是在观察性研究中。重要的是，方法论决策缺乏透明度正在威胁着现代研究的有效性和重复性。我们提出了一个实用的框架，用于处理和报告观察性研究中不完整数据的分析，我们使用雅芳亲子纵向研究的一个案例研究来说明这一点。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处理丢失的数据。一个重要的考虑因素是，完整记录的分析是否可能有效，多重归罪或替代方法是否可能提供好处，以及是否需要关于缺失机制的敏感性分析；2)检查数据，检查分析计划中概述的方法是否合适，并进行预先计划的分析；以及3)报告结果，包括对缺失数据的描述，关于如何处理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的结果。这一框架旨在支持研究人员系统地思考丢失的数据，并透明地报告对研究结果的潜在影响，从而增加对研究结果的信心和重复性。缺失数据在医学研究中无处不在。提供了指导，但丢失的数据仍然经常得不到适当的处理。我们提出了一个处理和报告不完整数据分析的框架。这一框架鼓励研究人员系统地思考丢失的数据。采用这一框架将增加研究结果的再现性。本文提供了一个非常需要的框架，用于处理和报告观察性研究中不完整数据的分析。该框架非常强调预先规划统计分析，并鼓励在报告研究结果时保持透明度。采用这一框架将增加对研究结果的信心和重现性。
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. Importantly, the lack of transparency around methodological decisions is threatening the validity and reproducibility of modern 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) Examine the data, checking the methods outlined in the analysis plan are appropriate, and conduct the preplanned analysis; and 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, therefore increasing the confidence in and reproducibility of research findings. Missing data are ubiquitous in medical research. Guidance is available, but missing data are still often not handled appropriately. We present a framework for handling and reporting analyses of incomplete data. This framework encourages researchers to think systematically about missing data. Adoption of this framework will increase the reproducibility of research findings. This article provides a much needed framework for handling and reporting the analysis of incomplete data in observational studies. The framework puts a strong emphasis on preplanning the statistical analysis and encourages transparency when reporting the results of a study. Adoption of this framework will increase the confidence in and reproducibility of research findings.
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影响因子：
2.3
作者：
Bartlett JW;Seaman SR;White IR;Carpenter JR;Alzheimer's Disease Neuroimaging Initiative*
通讯作者：
Alzheimer's Disease Neuroimaging Initiative*
影响因子：
105.7
作者：
Hippisley-Cox, Julia;Coupland, Carol;Brindle, Peter
通讯作者：
Brindle, Peter
影响因子：
4
作者：
Bell, Melanie L.;Fiero, Mallorie;Hsu, Chiu-Hsieh
通讯作者：
Hsu, Chiu-Hsieh
影响因子：
2.3
作者：
Lee KJ;Carlin JB
通讯作者：
Carlin JB
DOI：
10.1111/j.1467-985x.2006.00407.x
发表时间：
2006-01-01
影响因子：
2
作者：
Carpenter, James R.;Kenward, Michael G.;Vansteelandt, Stijn
通讯作者：
Vansteelandt, Stijn