Richesson Rachel L
Duke University School of Nursing, 2007 Pearson Bldg, 307 Trent Drive, Durham, NC 27710, United States.
J Biomed Inform. 2014 Dec;52:4-10. doi: 10.1016/j.jbi.2014.01.002. Epub 2014 Jan 14.
Medication exposure is an important variable in virtually all clinical research, yet there is great variation in how the data are collected, coded, and analyzed. Coding and classification systems for medication data are heterogeneous in structure, and there is little guidance for implementing them, especially in large research networks and multi-site trials. Current practices for handling medication data in clinical trials have emerged from the requirements and limitations of paper-based data collection, but there are now many electronic tools to enable the collection and analysis of medication data. This paper reviews approaches to coding medication data in multi-site research contexts, and proposes a framework for the classification, reporting, and analysis of medication data. The framework can be used to develop tools for classifying medications in coded data sets to support context appropriate, explicit, and reproducible data analyses by researchers and secondary users in virtually all clinical research domains.
药物暴露在几乎所有临床研究中都是一个重要变量,但在数据的收集、编码和分析方式上存在很大差异。药物数据的编码和分类系统在结构上是异质的,并且在实施这些系统方面几乎没有指导,尤其是在大型研究网络和多中心试验中。临床试验中处理药物数据的当前做法源于纸质数据收集的要求和局限性,但现在有许多电子工具可用于药物数据的收集和分析。本文回顾了在多中心研究背景下对药物数据进行编码的方法,并提出了一个药物数据分类、报告和分析的框架。该框架可用于开发工具,对编码数据集中的药物进行分类,以支持几乎所有临床研究领域的研究人员和二次使用者进行符合上下文、明确且可重复的数据分析。