1.Application of Linear Mixed Model Tree in Longitudinal Trajectory of Body Mass Index
Yiteng ZANG ; Sizhen CHEN ; Beier LU
Chinese Journal of Health Statistics 2024;41(1):41-44
Objective To understand the trajectory and classification of adult body mass index(BMI)in Jiangsu Province.Methods Based on China Health and Nutrition Survey,this study used the linear mixed model tree to explore the trajectory and classification of BMI of people aged 18-65 in Jiangsu Province.Results The linear mixed model tree had 13 nodes and the depth was 6.The classification nodes were baseline BMI,average calorie intake and baseline age.Conclusion The linear mixed model tree can identify the trajectory of BMI and expand the research method of longitudinal data.
2. Multiple imputation of missing data in clinical longitudinal studies and its sensitivity analyses
Zhigang JIAO ; Ru FAN ; Sizhen CHEN ; Yiteng ZANG ; Shiyuan WANG ; Bingwei CHEN ; Biyun XU
Chinese Journal of Clinical Pharmacology and Therapeutics 2021;26(9):1037-1041
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.