[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]
[Markov Chain Monte Carlo Method of multiple imputation for longitudinal data with missing values in the survey of maternal and children health].
作者信息
Mao Qun-xia, Li Xiao-song
机构信息
Department of Health Statistics, West China School of Public Health, Sichuan University, Chengdu 610041, China.
出版信息
Sichuan Da Xue Xue Bao Yi Xue Ban. 2005 May;36(3):422-5.
OBJECTIVE
To deal with arbitrary missing pattern in longitudinal data of the Survey of Maternal and Child Health and make the most appropriate inferences with multiple imputation (MI) for further analysis.
METHODS
SAS 9.0 was used for Markov Chain Monte Carlo (MCMC) method of MI procedure to impute missing values and combine inferences.
RESULTS
The result is acceptable as the data set was imputed 5 times.
CONCLUSION
MI is able to solve a variety of problems in missing data sets and to improve the statistical power, especially with the use of MCMC method, for complicated missing data sets.
目的
处理妇幼健康调查纵向数据中的任意缺失模式，并采用多重填补（MI）进行最恰当的推断以便进一步分析。
方法
使用SAS 9.0通过MI程序的马尔可夫链蒙特卡罗（MCMC）方法对缺失值进行填补并合并推断。
结果
由于数据集进行了5次填补，结果是可接受的。
结论
MI能够解决缺失数据集中的各种问题并提高统计效能，特别是对于复杂的缺失数据集，使用MCMC方法时更是如此。
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