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Chinese Journal of Clinical Pharmacology and Therapeutics ›› 2021, Vol. 26 ›› Issue (9): 1037-1041.doi: 10.12092/j.issn.1009-2501.2021.09.009

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Multiple imputation of missing data in clinical longitudinal studies and its sensitivity analyses

JIAO Zhigang 1, FAN Ru 1, XU Biyun 2, CHEN Sizhen 1, ZANG Yiteng 1, WANG shiyuan 1, CHEN Bingwei 1     

  1. 1 Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, Jiangsu, China; 2 Medical Statistics Analysis Center, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, Jiangsu, China
  • Received:2021-05-10 Revised:2021-07-27 Online:2021-09-26 Published:2021-09-30

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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.

Key words: multiple imputation, longitudinal data, sensitivity analysis

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