[Markov Chain Monte Carlo Method of multiple imputation for longitudinal data with missing values in the survey of maternal and children health].
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.


CONCLUSIONS
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.
Mao
Qun-xia
QX
Department of Health Statistics, West China School of Public Health, Sichuan University, Chengdu 610041, China.
Li
Xiao-song
XS
D015982:Bias; D002648:Child; D046688:Child Mortality; D002669:Child Welfare / Q000706:statistics & numerical data; D002675:Child, Preschool; D002681:China / Q000453:epidemiology; D003625:Data Collection / Q000379:methods / Q000592:standards; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D008137:Longitudinal Studies; D008297:Male; D008390:Markov Chains; D008430:Maternal-Child Health Centers / Q000706:statistics & numerical data*; D015233:Models, Statistical; D009010:Monte Carlo Method; D012107:Research Design