Missing data assumptions and methods in a smoking cessation study
Missing data assumptions and methods in a smoking cessation study
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DOI：
10.1111/j.1360-0443.2009.02809.x
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发表时间：
2010-03-01
期刊：
影响因子：
6
通讯作者：
Decker, Paul A.
中科院分区：
文献类型：
--
作者：
Barnes, Sunni A.;Larsen, Michael D.;Decker, Paul A.
在戒烟研究中，相当大比例的受试者对后续尝试没有反应。在戒烟文献中，通常的程序是假设非应答者已经重新开始吸烟。本研究使用来自高随访率研究的数据来评估不同的缺失数据输入方法可能造成的偏倚程度。设计和方法基于12个月时几乎没有缺失随访信息的大型数据集，进行了一项模拟研究，以比较和对比缺失数据的输入方法（假设吸烟、倾向评分匹配和最优匹配）在各种假设下的缺失数据是如何产生的（随机产生的缺失值、吸烟者不响应的增加以及两者的混合）。缺失数据的输入方法都会导致一定程度的偏差，并且随着缺失数据量的增加而增加。结论当存在大量缺失数据时，现有的缺失数据输入方法均不能补偿偏倚。
AimA sizable percentage of subjects do not respond to follow-up attempts in smoking cessation studies. The usual procedure in the smoking cessation literature is to assume that non-respondents have resumed smoking. This study used data from a study with a high follow-up rate to assess the degree of bias that may be caused by different methods of imputing missing data.Design and methodsBased on a large data set with very little missing follow-up information at 12 months, a simulation study was undertaken to compare and contrast missing data imputation methods (assuming smoking, propensity score matching and optimal matching) under various assumptions as to how the missing data arose (randomly generated missing values, increased non-response from smokers and a hybrid of the two).FindingsMissing data imputation methods all resulted in some degree of bias which increased with the amount of missing data.ConclusionNone of the missing data imputation methods currently available can compensate for bias when there are substantial amounts of missing data.