Strategies and applications to address variable abundance differences in multicenter drug safety studies

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Author: Yun-Xiao WU ¹ Lu XU ² Lin ZHUO ² Sheng-Feng WANG ¹  Si-Yan ZHAN ^1, ²

Affiliation: 1. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, Chi-na 2. Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing 100191, China

Keywords: Drug safety Multi-center Variables missing Transfer learning

DOI: 10.19960/j.issn.1005-0698.202305012

Reference: Yun-Xiao WU, Lu XU, Lin ZHUO, Sheng-Feng WANG, Si-Yan ZHAN.Strategies and applications to address variable abundance differences in multicenter drug safety studies[J].Yaowu Liuxingbingxue Zazhi,2023, 32(5): 575-581.DOI: 10.19960/j.issn.1005-0698.202305012.[Article in Chinese]  Copied

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Abstract

When adverse events of interest for post-marketing drug safety studies are relatively rare, conducting multi-center studies is necessary to address this issue. However, multi-center studies are often trapped in the problem of different central variables with varying degrees of richness, especially the complete lack of key variables in sub-centers, which causes studies not to make full use of all the information of each center when adjusting bias. Pro-pensity score calibration (PSC) and transfer learning proposed in recent years are available to deal with the complete absence of some variables in the sub-center. PSC has been applied to pharmacoepidemiology, but no report of transfer learning in this field has been published. This article will outline the characteristics and applications of the two methods, and focus on combing several types of transfer learning methods that can be used to solve such problems, and provide a reference for the in-depth study of transfer learning in multi-center drug safety evaluation.

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