Methodology to assess clinical liver safety data.
Methodology to assess clinical liver safety data.
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DOI：
10.1007/s40264-014-0184-5
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发表时间：
2014-11
期刊：
影响因子：
4.2
通讯作者：
Watkins, Paul B.
中科院分区：
文献类型：
--
作者：
Merz, Michael;Lee, Kwan R.;Kullak-Ublick, Gerd A.;Brueckner, Andreas;Watkins, Paul B.
肝脏安全性数据的分析本质上必须是多变量的，需要考虑观察结果的时间依赖性。目前用于肝脏安全性评估的标准工具（如汇总表、个体数据列表和叙述）仅在有限程度上满足了这些要求。在系统化工作流程的背景下使用图形（包括预定义的图形模板）是对标准工具的宝贵补充，有助于确保评估的完整性，并支持假设生成和检验。交互式地使用图形工作流允许在基于团队的环境中进行分析，并有助于识别最适合发布和监管报告的图形。另一个重要的工具是统计离群值检测，考虑到这样一个事实，即对于药物性肝损伤的评估，极端值的识别和全面评价比数据中集中趋势的测量更具相关性。总之，系统的图形数据探索和统计离群值检测可能有可能显著改善临床肝脏安全性数据的评估和解释。召开了一次研讨会，讨论在临床试验中评估药物性肝损伤（DILI）的最佳实践。
Analysis of liver safety data has to be multivariate by nature and needs to take into account time dependency of observations. Current standard tools for liver safety assessment such as summary tables, individual data listings, and narratives address these requirements to a limited extent only. Using graphics in the context of a systematic workflow including predefined graph templates is a valuable addition to standard instruments, helping to ensure completeness of evaluation, and supporting both hypothesis generation and testing. Employing graphical workflows interactively allows analysis in a team-based setting and facilitates identification of the most suitable graphics for publishing and regulatory reporting. Another important tool is statistical outlier detection, accounting for the fact that for assessment of Drug-Induced Liver Injury, identification and thorough evaluation of extreme values has much more relevance than measures of central tendency in the data. Taken together, systematical graphical data exploration and statistical outlier detection may have the potential to significantly improve assessment and interpretation of clinical liver safety data. A workshop was convened to discuss best practices for the assessment of drug-induced liver injury (DILI) in clinical trials.
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