Quantitative Safety Monitoring in Clinical Trials: Application of Multiple Statistical Methodologies for Infrequent Even
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ANALYTICAL REPORT
Quantitative Safety Monitoring in Clinical Trials: Application of Multiple Statistical Methodologies for Infrequent Events Jiabu Ye, Ph.D.1 · Shihua Wen, Ph.D.2 · John Schoenfelder, Ph.D.3 · Syed Islam, MD, Dr.PH4 Received: 2 January 2020 / Accepted: 6 March 2020 © The Drug Information Association, Inc 2020
Abstract Background There are limited quantitative approaches for evaluating rare safety outcomes from controlled clinical trials in either a blinded or unblinded setting. This manuscript demonstrates an application of three statistical methods for quantitative safety monitoring that can be implemented during any phase of a clinical trial, including open-label extension studies. Methods An interactive safety monitoring (iSM) tool was developed using R language in the publicly available R-Shiny app and was implemented for three statistical methods of quantitative safety monitoring. These methods are sequential probability ratio test (SPRT), maximized SPRT (MaxSPRT), and Bayesian posterior probability threshold (BPPT). The iSM tool evaluated specific safety signals that incorporated pre-specified background rates or reference risk ratios. Results Two sets of blinded clinical trial data were used for case studies to demonstrate the use the iSM tool. Two particular adverse events, myocardial infarction (MI) and serious infection, were monitored. Monte Carlo simulation was conducted to evaluate the operating characteristics of pre-specified parameters. It showed that after adjusting for exposure, the BPPT and MaxSPRT yielded similar results in identifying a pre-specified signals while the SPRT method failed to detect such signals. Conclusion Statistical methods shown for the case studies, as well as the application of the user-friendly iSM tool, greatly enhance the quantitative monitoring of safety events of interest in ongoing clinical trials The BPPT and MaxSPRT methods seem more sensitive in picking-up early signals than the SPRT method when the number of safety events is small. Keywords Safety monitoring tool · Quantitative methods
Background
* Syed Islam [email protected] Jiabu Ye [email protected] Shihua Wen [email protected] John Schoenfelder [email protected] 1
AstraZeneca Pharmaceuticals, One Medimmune Way, Gaithersburg, MD 20878, USA
2
Novartis Pharmaceuticals Corporation, One Health Plaza, East Hanover, NJ 07936, USA
3
AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, USA
4
Jazz Pharmaceuticals, 2005 Market Street, Suite 2100, Philadelphia, PA 19103, USA
Monitoring adverse events (AEs) during clinical trials is important for understanding the safety of investigational drugs. However, the number of subjects exposed to treatment is often limited and consequently the numbers of observed adverse events are inadequate for conclusive assessment. Although well-developed quantitative methods are available to evaluate efficacy endpoints (often statistically powered by design), there are only limited quantitative approaches to eva
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