Characteristics of hospital differences in missing of clinical laboratory test results in a multi-hospital observational database contributing to MID-NET® in Japan

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

BACKGROUND

To evaluate missing data methods applied to laboratory test results used for confounding adjustment, utilizing data from 10 MID-NET®-collaborative hospitals.

METHODS

Using 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.

RESULTS

In 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.

CONCLUSIONS

SRI 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.

Regulatory Approval With Real-World Data From Regulatory Science Perspective in Japan

Recently, there has been a growing trend in clinical development to utilize real-world data (RWD) to improve the efficiency of drug/medical device development. Especially, the use of RWD to generate real-world evidence (RWE) in regulatory approval is currently undergoing a period of great change with an increasing degree of active discussion. In Japan, RWE has been used in the control arms of clinical trials, observational studies, post-marketing surveillance, and public knowledge-based applications for regulatory approval. However, the exclusive use of RWE applications has still not been applied. In this paper, we summarize the history and the current situation of RWE and focus on the utilization for the purpose of regulatory approval. In addition, we will discuss the issues and perspectives for registry research in the utilization for regulatory approval in Japan.