Missing data: a special challenge in aging research.
Missing data: a special challenge in aging research.
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DOI：
10.1111/j.1532-5415.2008.02168.x
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发表时间：
2009-04
影响因子：
6.3
通讯作者：
Studenski SA
中科院分区：
文献类型：
--
作者：
Hardy SE;Allore H;Studenski SA
有关老年人护理的证据为实践提供了信息，但受到特殊方法挑战的影响。缺失数据，从问卷中缺少个别项目到完全失去随访，影响证据的质量，更可能发生在老年人的研究中，因为老年人有更多的健康和功能问题，干扰了数据收集的各个方面。本文的目的是促进对临床衰老研究中缺失数据的风险和后果的认识，并提供一种有组织的预防和管理方法。虽然几乎永远不可能实现完整的数据捕获，但防止数据丢失的努力比分析性“治疗”更有效。防止数据缺失的策略包括：1）选择易于确定和设计有效替代定义的主要结局; 2）根据目标人群的特殊需求调整数据收集; 3）对数据收集计划进行试点测试; 4）在研究期间监测数据缺失率，并根据需要调整数据收集程序。缺失数据分析的关键步骤包括：1）在分析前评估缺失数据的程度和类型; 2）探索导致缺失数据的潜在机制; 3）使用多种分析方法评估缺失数据对结果的影响。受试者应1）披露缺失数据和失访率，2）将脱落率与完成研究的受试者进行比较，3）描述在分析阶段如何管理缺失数据，4）讨论缺失数据对研究结论的潜在影响。
Evidence about care of older adults informs practice but is influenced by special methodological challenges. Missing data, ranging from lack of individual items in questionnaires to complete loss to follow up, affect the quality of the evidence and are more likely to occur in studies of older adults because older adults have more health and functional problems that interfere with all aspects of data collection. The purpose of this article is to promote knowledge about the risks and consequences of missing data in clinical aging research, and to provide an organized approach to prevention and management. While it is almost never possible to achieve complete data capture, efforts to prevent missing data are more effective than analytic “cure”. Strategies to prevent missing data include 1) selecting a primary outcome that is easy to determine and devise valid alternate definitions, 2) adapting data collection to the special needs of the target population, 3) pilot testing data collection plans, and 4) monitoring missing data rates during the study and adapt data collection procedures as needed. Key steps in the analysis of missing data include 1) assessing the extent and types of missing data prior to analysis, 2) exploring potential mechanisms that contributed to the missing data, and 3) using multiple analytic approaches to assess the effect of missing data on the results. Manuscripts should 1) disclose rates of missing data and losses to follow up, 2) compare drop outs to participants who completed the study, 3) describe how missing data was managed in the analysis phase, and 4) discuss the potential impact of missing data on the conclusions of the study.
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影响因子：
7.2
作者：
Diehr, P;Williamson, J;Psaty, BM
通讯作者：
Psaty, BM
影响因子：
--
作者：
Fried, TR;Byers, AL;Dubin, JA
通讯作者：
Dubin, JA
DOI：
10.7326/0003-4819-139-5_part_2-200309021-00007
发表时间：
2003-09-02
影响因子：
39.2
作者：
Diehr, P;Patrick, DL
通讯作者：
Patrick, DL
影响因子：
5
作者：
Magaziner, J;Zimmerman, SI;Fox, KM
通讯作者：
Fox, KM
影响因子：
7.2
作者：
Cassidy, EL;Baird, E;Sheikh, JI
通讯作者：
Sheikh, JI