Methodology to Assess Clinical Liver Safety Data

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. Usin...

Full description

Saved in:
Bibliographic Details
Published in:Drug safety 2014-11, Vol.37 (Suppl 1), p.33-45
Main Authors: Merz, Michael, Lee, Kwan R., Kullak-Ublick, Gerd A., Brueckner, Andreas, Watkins, Paul B.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Biomarkers
Colleges & universities
Drug Safety and Pharmacovigilance
Drug toxicity and drugs side effects treatment
Laboratories
Liver
Medical sciences
Medicine
Medicine & Public Health
Patients
Pharmacology. Drug treatments
Pharmacology/Toxicology
Product safety
Public private partnerships
Review
Review Article
Statistical methods
Toxicity: digestive system
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0114-5916
1179-1942
DOI:10.1007/s40264-014-0184-5