The application of multiple imputation and multilevel model in longitudinal follow-up data

WU Qiu-hong; ZHANG Pi-de; ZHOU Guo-mao; LUO Zhen-zhou

doi:10.16462/j.cnki.zhjbkz.2016.07.021

Volume 20 Issue 7

Jul. 2016

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2016 > 20(7): 729-733

WU Qiu-hong, ZHANG Pi-de, ZHOU Guo-mao, LUO Zhen-zhou. The application of multiple imputation and multilevel model in longitudinal follow-up data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2016, 20(7): 729-733. doi: 10.16462/j.cnki.zhjbkz.2016.07.021

Citation:

WU Qiu-hong, ZHANG Pi-de, ZHOU Guo-mao, LUO Zhen-zhou. The application of multiple imputation and multilevel model in longitudinal follow-up data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2016, 20(7): 729-733. doi: 10.16462/j.cnki.zhjbkz.2016.07.021

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The application of multiple imputation and multilevel model in longitudinal follow-up data

doi: 10.16462/j.cnki.zhjbkz.2016.07.021

1. Department of Skin Venereal Disease Prevention and Control, Nanshan District Hospital for Chronic Disease Prevention and Treatment, Shenzhen 518054, China;
2. Department of Health Statistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510310, China

Received Date: 2015-07-14
Rev Recd Date: 2016-01-15

Abstract

Abstract

Objective To discuss how to deal with missing data and intra-class correlation in longitudinal follow-up data, and fully use the collected data to reflect the overall information.Methods Firstly, longitudinal complete data and missing data were simulated; Secondly, apply multiple imputation methods(MI) and multilevel model(MLM) were applied to deal with these data sets; Moreover, randomized block analyze of variance was used to analysis each data sets; Finally, an example was used to validate the simulation results.Results The results of different missing type coincided with those of different missing proportion: the deviation of MI with MLM was less than simple MLM. The more filling times, the smaller the deviation. As the missing rate increased, deviation of all methods became larger. The results got more stable in the present of large sample size. The simulation results were also verified by example analysis.Conclusions MI with MLM can provide effective and reasonable results in the longitudinal follow-up data.
- Longitudinal study,
- Model,Statistical,
- Follow-up studies

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References(18)

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