A Research on Metrics for Covariate Balance in Real World Study
10.11783/j.issn.1002-3674.2023.06.009
- VernacularTitle:真实世界研究中协变量组间均衡性的诊断指标研究
- Author:
Wenwen WANG
1
;
Yukun YUAN
;
Yujia LI
Author Information
1. 空军军医大学军事预防医学系军事卫生学教研室(714200)
- Keywords:
Real world study;
Covariate balance;
Metrics for balance;
Propensity score
- From:
Chinese Journal of Health Statistics
2023;40(6):841-845,851
- CountryChina
- Language:Chinese
-
Abstract:
Objective To compare the diagnostic index which used to evaluate the balance of covariates between groups in real world study(RWS).Methods Simulate RWS simulation data scenarios such as different inter group equilibrium degree,different covariate and exposure,outcome relationship,etc.by constructing the correlation model between each diagnostic index and estimation deviation,evaluate the accuracy and robustness of different single covariate and global covariate diagnostic indexes.Results In addition to L1 measure,standardized difference,overlap coefficient,K-S distance,Lévy distance,Mahalanobis distance and general weighted difference can distinguish different degrees of inter group covariate equilibrium.The R2 of the correlation models estimated by the C-statistic based on propensity score and the general weighted difference diagnostic index is greater than 0.8,and the intercept value approaches the origin,which is the most accurate and stable for the diagnosis of inter group equilibrium.Conclusion The single covariate diagnosis index can evaluate the balance of covariates between groups of RWS data,but the accuracy,sensitivity and robustness of the global diagnosis index are better.Among them,the diagnosis effect of L1 measure is poor,while of the C-statistic based on propensity score is the best.
