Comparison of different methods in dealing with HIV viral load data with diversified missing value mechanism on HIV positive MSM
10.3760/cma.j.issn.0254-6450.2017.11.025
- VernacularTitle:MSM人群HIV感染者病毒载量抽样调查缺失数据填补方法研究
- Author:
Zhen JIANG
1
;
Zhi DOU
;
Weilu SONG
;
Jie XU
;
Zunyou WU
Author Information
1. 中国疾病预防控制中心性病艾滋病预防控制中心预防干预室
- Keywords:
HIV;
Viral load;
Missing data;
Multiple imputation;
Markov Chain Monte Carlo
- From:
Chinese Journal of Epidemiology
2017;38(11):1563-1568
- CountryChina
- Language:Chinese
-
Abstract:
Objective To compare results of different methods in organizing HIV viral load (VL) data with missing values mechanism. Methods We used software SPSS 17.0 to simulate complete and missing data with different missing value mechanism from HIV viral loading data collected from MSM in 16 cities in China in 2013. Maximum Likelihood Methods Using the Expectation and Maximization Algorithm (EM), regressive method, mean imputation, delete method, and Markov Chain Monte Carlo (MCMC) were used to supplement missing data respectively. The results of different methods were compared according to distribution characteristics, accuracy and precision. Results HIV VL data could not be transferred into a normal distribution. All the methods showed good results in iterating data which is Missing Completely at Random Mechanism (MCAR). For the other types of missing data, regressive and MCMC methods were used to keep the main characteristic of the original data. The means of iterating database with different methods were all close to the original one. The EM, regressive method, mean imputation, and delete method under-estimate VL while MCMC overestimates it. Conclusion MCMC can be used as the main imputation method for HIV virus loading missing data. The iterated data can be used as a reference for mean HIV VL estimation among the investigated population.
