Multiple imputation of missing data in clinical longitudinal studies and its sensitivity analyses
10.12092/j.issn.1009-2501.2021.09.009
- Author:
Zhigang JIAO
1
;
Ru FAN
1
;
Sizhen CHEN
1
;
Yiteng ZANG
1
;
Shiyuan WANG
1
;
Bingwei CHEN
1
;
Biyun XU
2
Author Information
1. Department of Epidemiology and Health Statistics, School of Public Health, Southeast University
2. Medical Statistics Analysis Center, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School
- Publication Type:Journal Article
- Keywords:
Longitudinal data;
Multiple imputation;
Sensitivity analysis
- From:
Chinese Journal of Clinical Pharmacology and Therapeutics
2021;26(9):1037-1041
- CountryChina
- Language:Chinese
-
Abstract:
AIM: To guide the multiple imputation of missing data in clinical longitudinal studies and its sensitivity analyses, and highlight the importance of sensitivity analyses by taking the clinical trial of Qizhitongluo Capsule in treating ischemic stroke as an example. METHODS: To implement PROC MI process in SAS to perform multiple imputation and its sensitivity analysis. RESULTS: In the example, after multiple imputation, improvements in lower limb motor scores of the Qizhitongluo group were greater than those of the placebo group (all P<0.01), and the results of two sensitivity analyses under "missing not at random" were consistent with those under "missing at random". CONCLUSION: Multiple imputations combined with sensitivity analyses can ensure a robust result. It is recommended that clinical researchers perform sensitivity analyses after filling missing data.
