Dealing with missing data in laboratory test results used as a baseline covariate: results of multi-hospital cohort studies utilizing a database system contributing to MID-NET® in Japan

BMC Medical Informatics and Decision Making

Research

Tosiya Sato¹  Maki Komamine²  Masatomo Omiya³  Yoshiaki Fujimura⁴ 

[1] Department of Biostatistics, Kyoto University School of Public Health, Yoshida-konoecho, Sakyo-ku, 606-8501, Kyoto, Japan;Department of Biostatistics, Kyoto University School of Public Health, Yoshida-konoecho, Sakyo-ku, 606-8501, Kyoto, Japan;Office of Medical Informatics and Epidemiology, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan;Department of Clinical Biostatistics, Graduate School of Medicine, Kyoto University, Kyoto, Japan;Head Office, Tokushukai Information System Incorporated, Osaka, Japan;

关键词: Pharmacoepidemiology;    Observational data;    Database;    Laboratory test item;    Missing data;   

DOI  :  10.1186/s12911-023-02345-7

 received in 2022-11-30, accepted in 2023-10-19,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundTo evaluate missing data methods applied to laboratory test results used for confounding adjustment, utilizing data from 10 MID-NET®-collaborative hospitals.MethodsUsing two scenarios, five methods dealing with missing laboratory test results were applied, including three missing data methods (single regression imputation (SRI), multiple imputation (MI), and inverse probability weighted (IPW) method). We compared the point estimates of adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) between the five methods. Hospital variability in missing data was considered using the hospital-specific approach and overall approach. Confounding adjustment methods were propensity score (PS) weighting, PS matching, and regression adjustment.ResultsIn Scenario 1, the risk of diabetes due to second-generation antipsychotics was compared with that due to first-generation antipsychotics. The aHR adjusted by PS weighting using SRI, MI, and IPW by the hospital-specific-approach was 0.61 [95%CI, 0.39–0.96], 0.63 [95%CI, 0.42–0.93], and 0.76 [95%CI, 0.46–1.25], respectively. In Scenario 2, the risk of liver injuries due to rosuvastatin was compared with that due to atorvastatin. Although PS matching largely contributed to differences in aHRs between methods, PS weighting provided no substantial difference in point estimates of aHRs between SRI and MI, similar to Scenario 1. The results of SRI and MI in both scenarios showed no considerable changes, even upon changing the approaches considering hospital variations.ConclusionsSRI and MI provide similar point estimates of aHR. Two approaches considering hospital variations did not markedly affect the results. Adjustment by PS matching should be used carefully.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 2

Fig. 5

12951_2015_155_Article_IEq47.gif

Fig. 5