缺失数据：我们是否应该关注？
Mi??ing data: should we c?re?
机构信息
Department of Statistics at the University of Connecticut, Storrs, CT 06269-4120, USA.
出版信息
Am J Public Health. 2013 Feb;103(2):200-1. doi: 10.2105/AJPH.2012.300904. Epub 2012 Dec 13.
Abstract
Across all fields of research, the issue of missing data arises. In most studies, surveys, or experiments, there are instances of nonresponse that need to be appropriately dealt with to conduct reasonable statistical analyses. There are several types of missing data as well as ways to deal with incomplete data sets.
摘要
在所有研究领域中，都会出现数据缺失的问题。在大多数研究、调查或实验中，都存在需要适当处理的无响应情况，以便进行合理的统计分析。存在几种类型的数据缺失以及处理不完整数据集的方法。
相似文献
1
Mi??ing data: should we c?re?缺失数据：我们是否应该关注？
Am J Public Health. 2013 Feb;103(2):200-1. doi: 10.2105/AJPH.2012.300904. Epub 2012 Dec 13.
2
[Markov Chain Monte Carlo Method of multiple imputation for longitudinal data with missing values in the survey of maternal and children health].[妇幼健康调查中具有缺失值的纵向数据多重填补的马尔可夫链蒙特卡罗方法]
Sichuan Da Xue Xue Bao Yi Xue Ban. 2005 May;36(3):422-5.
3
Evaluating survey quality in health services research: a decision framework for assessing nonresponse bias.评估卫生服务研究中的调查质量：评估无应答偏倚的决策框架。
Health Serv Res. 2013 Jun;48(3):913-30. doi: 10.1111/1475-6773.12002. Epub 2012 Oct 10.
4
Applications of multiple imputation in medical studies: from AIDS to NHANES.多重填补在医学研究中的应用：从艾滋病到美国国家健康与营养检查调查
Stat Methods Med Res. 1999 Mar;8(1):17-36. doi: 10.1177/096228029900800103.
5
Accounting for missing data in statistical analyses: multiple imputation is not always the answer.在统计分析中处理缺失数据：多重插补并不总是答案。
Int J Epidemiol. 2019 Aug 1;48(4):1294-1304. doi: 10.1093/ije/dyz032.
6
Multiple imputation in veterinary epidemiological studies: a case study and simulation.兽医流行病学研究中的多重填补：一个案例研究与模拟
Prev Vet Med. 2016 Jul 1;129:35-47. doi: 10.1016/j.prevetmed.2016.04.003. Epub 2016 May 13.
7
Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study.预后建模研究中缺失协变量数据处理技术的比较：一项模拟研究。
BMC Med Res Methodol. 2010 Jan 19;10:7. doi: 10.1186/1471-2288-10-7.
8
Multiple imputation for patient reported outcome measures in randomised controlled trials: advantages and disadvantages of imputing at the item, subscale or composite score level.在随机对照试验中对患者报告结局测量进行多重插补：在项目、分量表或综合评分层面插补的优缺点。
BMC Med Res Methodol. 2018 Aug 28;18(1):87. doi: 10.1186/s12874-018-0542-6.
9
Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study.比较群体平均模型和聚类特异性模型在分析缺失二分类结局的整群随机试验中的应用：一项模拟研究。
BMC Med Res Methodol. 2013 Jan 23;13:9. doi: 10.1186/1471-2288-13-9.
10
Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?在二元结局观察值缺失的情况下，对于估计随机对照试验中的患病率（风险）差异，使用多重填补法是否比完全病例分析法更好？
Trials. 2016 Jul 22;17:341. doi: 10.1186/s13063-016-1473-3.
引用本文的文献
1
Multiple Imputation for Incomplete Data in Environmental Epidemiology Research.环境流行病学研究中不完全数据的多重插补。
Curr Environ Health Rep. 2019 Jun;6(2):62-71. doi: 10.1007/s40572-019-00230-y.
2
Principled Approaches to Missing Data in Epidemiologic Studies.原则性方法在流行病学研究中的缺失数据处理。
Am J Epidemiol. 2018 Mar 1;187(3):568-575. doi: 10.1093/aje/kwx348.
本文引用的文献
1
Are we missing the importance of missing values in HIV prevention randomized clinical trials? Review and recommendations.我们是否忽略了 HIV 预防随机临床试验中缺失值的重要性？综述与建议。
AIDS Behav. 2012 Aug;16(6):1382-93. doi: 10.1007/s10461-011-0125-6.
2
Missing data: what a little can do, and what researchers can do in response.缺失数据：其微小作用及研究人员的应对措施
Am J Ophthalmol. 2009 Dec;148(6):820-2. doi: 10.1016/j.ajo.2009.07.027.
Zotero 插件