Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Ryals Building, Room 327D, 1665 University Boulevard, Birmingham, AL 35294-0022, USA.
Am J Epidemiol. 2011 Sep 15;174(6):718-26. doi: 10.1093/aje/kwr155. Epub 2011 Jul 29.
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.
纵向队列研究通常通过检索病历来识别和裁定随访期间发现的新发病例。有几个原因可能导致疑似病例的裁定过程无法顺利完成，包括无法从医院检索病历，以及疑似病例和数据分析之间的时间间隔不足。这些“未完成的裁定”通常被假定为不是事件，这种方法可能会导致精度损失和偏差。在本文中，作者评估了使用多种插补方法来分析不完整的裁定。使用 2008-2009 年地理和种族差异中风原因研究（REGARDS）的数据，作者证明这种方法可以提高风险因素与发病事件之间关系估计的精度，并减少偏差。
