Exploratory Analysis of Clinical Safety Data to Detect Safety Signals

RCT

safety

drug-evaluation

It is difficult to design a clinical study to provide sound inferences about safety effects of drugs in addition to providing trustworthy evidence for efficacy. Patient entry criteria and experimental design are targeted at efficacy, and there are too many possible safety endpoints to be able to control type I error while preserving power. Safety analysis tends to be somewhat ad hoc and exploratory. But with the large quantity of safety data acquired during clinical drug testing, safety data are rarely harvested to their fullest potential. Also, decisions are sometimes made that result in analyses that are somewhat arbitrary or that lose statistical efficiency. For example, safety assessments can be too quick to rely on the proportion of patients in each treatment group at each clinic visit who have a lab measurement above two or three times the upper limit of normal.

Safety reports frequently fail to fully explore areas such as
• which types of patients are having AEs?
• what distortions in the tails of the distribution of lab values are taking place?
• which AEs tend to occur in the same patient?
• how to clinical AEs correlate to continuous lab measurements at a given time
• which AEs and lab abnormalities are uniquely related to treatment assigned?
• do preclinically significant measurements at an earlier visit predict AEs at a later visit?
• how can time trends in many variables be digested into an understandable picture?

This talk will demonstrate some of the exploratory statistical and graphical methods that can help answer questions such as the above, using examples based on data from real pharmaceutical trials.

Author

Frank Harrell

Published

June 8, 2006

GlaxoSmithKline Biostatistics Advisory Board Meeting, Research Triangle Park NC 2002-10-29
Duke Clinical Research Institute Research Conference, 2003-03-25
Biometrics Research at Merck Research Labs 2003-08-20
Contributed paper to the Society for Clinical Trials 2004-05-24
Invited presentation for the Lincoln Technologies Data Visualization Workshop 2005-07-01
Invited presentation for the FDA Visiting Professor Lecture Series 2005-10-11
Department of Statistics, University of Glasgow, Scotland 2006-06-08
Slides
Code

--- title: Exploratory Analysis of Clinical Safety Data to Detect Safety Signals author: - name: Frank Harrell url: https://hbiostat.org date: 2006-06-08 categories: [RCT, safety, drug-evaluation] description: "It is difficult to design a clinical study to provide sound inferences about safety effects of drugs in addition to providing trustworthy evidence for efficacy. Patient entry criteria and experimental design are targeted at efficacy, and there are too many possible safety endpoints to be able to control type I error while preserving power. Safety analysis tends to be somewhat ad hoc and exploratory. But with the large quantity of safety data acquired during clinical drug testing, safety data are rarely harvested to their fullest potential. Also, decisions are sometimes made that result in analyses that are somewhat arbitrary or that lose statistical efficiency. For example, safety assessments can be too quick to rely on the proportion of patients in each treatment group at each clinic visit who have a lab measurement above two or three times the upper limit of normal. Safety reports frequently fail to fully explore areas such as • which types of patients are having AEs? • what distortions in the tails of the distribution of lab values are taking place? • which AEs tend to occur in the same patient? • how to clinical AEs correlate to continuous lab measurements at a given time • which AEs and lab abnormalities are uniquely related to treatment assigned? • do preclinically significant measurements at an earlier visit predict AEs at a later visit? • how can time trends in many variables be digested into an understandable picture? This talk will demonstrate some of the exploratory statistical and graphical methods that can help answer questions such as the above, using examples based on data from real pharmaceutical trials." --- * GlaxoSmithKline Biostatistics Advisory Board Meeting, Research Triangle Park NC 2002-10-29 * Duke Clinical Research Institute Research Conference, 2003-03-25 * Biometrics Research at Merck Research Labs 2003-08-20 * Contributed paper to the Society for Clinical Trials 2004-05-24 * Invited presentation for the Lincoln Technologies Data Visualization Workshop 2005-07-01 * Invited presentation for the FDA Visiting Professor Lecture Series 2005-10-11 * Department of Statistics, University of Glasgow, Scotland 2006-06-08 * [Slides](https://hbiostat.org/talks/gsksafety.pdf) * [Code](https://hbiostat.org/R/talks/gsksafety.r)