Quantitative Safety Monitoring in Clinical Trials: Application of Multiple Statistical Methodologies for Infrequent Events.

Abstract

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. 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. 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. 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.