Yokoyama Satoshi, Hosomi Kouichi
Division of Drug Informatics, School of Pharmacy, Kindai University.
Yakugaku Zasshi. 2023;143(6):497-500. doi: 10.1248/yakushi.22-00179-3.
With the development of information technology, patient information is stored as electronic data, and huge amounts of such data are collected every day. Such a collection compiled over the course of clinical practice is called real-world data and is expected to be used for evaluating drug efficacy and safety. Real-world data such as health insurance association-based administrative claims databases, pharmacy-based dispensing databases, and spontaneous reporting system databases are mainly used in pharmaceutical research. Among them, claims databases are used for various observational studies such as studies on nationwide prescription trends, pharmacovigilance studies, and studies on rare diseases due to their large sample size. Although the nature of omics data is different from that of real-world data, it has become accessible on cloud platforms and are being used to broaden the scope of research in recent years. In this paper, we introduce a method for generating and further testing hypotheses through integrated analysis of real-world data and omics data, with a focus on administrative claims databases.
随着信息技术的发展，患者信息以电子数据形式存储，每天都会收集大量此类数据。在临床实践过程中汇编的此类数据集称为真实世界数据，有望用于评估药物疗效和安全性。基于健康保险协会的行政索赔数据库、基于药房的配药数据库和自发报告系统数据库等真实世界数据主要用于药物研究。其中，索赔数据库因其样本量大，可用于各种观察性研究，如全国处方趋势研究、药物警戒研究和罕见病研究。尽管组学数据的性质与真实世界数据不同，但近年来它已可在云平台上获取并被用于拓宽研究范围。在本文中，我们介绍一种通过对真实世界数据和组学数据进行综合分析来生成并进一步检验假设的方法，重点关注行政索赔数据库。
