Imputation of Incident Events in Longitudinal Cohort Studies
Imputation of Incident Events in Longitudinal Cohort Studies
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DOI：
10.1093/aje/kwr155
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发表时间：
2011-09-15
影响因子：
5
通讯作者：
Tiwari, Hemant K.
中科院分区：
文献类型：
--
作者：
Howard, George;McClure, Leslie A.;Tiwari, Hemant K.
纵向队列研究通常通过检索医疗记录来确定和裁决随访期间发现的事件。对疑似事件的裁决程序可能无法顺利完成有几个原因，包括无法从医院检索医疗记录以及疑似事件与数据分析之间的时间不足。这些“不完整的裁决”通常被认为不是事件，这种方法可能与准确性的丧失和偏见的引入有关。在这篇文章中，作者评估了多种imputation方法的使用，这些方法旨在包括分析中的不完整裁决。利用2008-2009年中风的地理和种族差异（REGARDS）研究的数据，他们证明这种方法可以提高准确性，减少风险因素和事件事件之间关系的估计偏差。
Longitudinal cohort studies normally identify and adjudicate incident events detected during follow-up by retrieving medical records. There are several reasons why the adjudication process may not be successfully completed for a suspected event including the inability to retrieve medical records from hospitals and an insufficient time between the suspected event and data analysis. These "incomplete adjudications" are normally assumed not to be events, an approach which may be associated with loss of precision and introduction of bias. In this article, the authors evaluate the use of multiple imputation methods designed to include incomplete adjudications in analysis. Using data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study, 2008-2009, they demonstrate that this approach may increase precision and reduce bias in estimates of the relations between risk factors and incident events.