Control charts for monitoring accumulating adverse event count frequencies from single and multiple blinded trials

Abstract

Conventional practice monitors accumulating information about drug safety in terms of the numbers of adverse events reported from trials in a drug development program. Estimates of between-treatment adverse event risk differences can be obtained readily from unblinded trials with adjustment for differences among trials using conventional statistical methods. Recent regulatory guidelines require monitoring the cumulative frequency of adverse event reports to identify possible between-treatment adverse event risk differences without unblinding ongoing trials. Conventional statistical methods for assessing between-treatment adverse event risks cannot be applied when the trials are blinded. However, CUSUM charts can be used to monitor the accumulation of adverse event occurrences. CUSUM charts for monitoring adverse event occurrence in a Bayesian paradigm are based on assumptions about the process generating the adverse event counts in a trial as expressed by informative prior distributions. This article describes the construction of control charts for monitoring adverse event occurrence based on statistical models for the processes, characterizes their statistical properties, and describes how to construct useful prior distributions. Application of the approach to two adverse events of interest in a real trial gave nearly identical results for binomial and Poisson observed event count likelihoods. Copyright © 2016 John Wiley & Sons, Ltd.

Using the Bayesian detection of potential risk using inference on blinded safety data (BDRIBS) method to support the decision to refer an event for unblinded evaluation

“…So, before combining multiple studies for blinded monitoring using BDRIBS, these factors should be considered to ensure the studies are similar enough for the purpose of combined blinded monitoring. While other methods that apply a Bayesian methodology to blinded signal detection exist [12][13][14] and more rigorous time-to-event analysis may be applied to unblinded data, the BDRIBS approach to detect safety signals from blinded data comes with simple model assumptions and easy-to-use software for convenient implementation.…”

Significance of Simultaneous Use of Different Methods for Interpretation of Internal Quality Control Data An Experience of a Tertiary Care Hospital

“…However conventional statistical method cannot be applied on blinded trials. [12] Righi L et al used CUSUM controls charts for monitoring of antimicrobial resistance and found its use to be complimentary for the hospital infection control strategies. [13] Rasmussen TB et al applied risk adjusted CUSUM charts for the monitoring of the 30 day hospital mortality and found that a alarm from CUSUM chart can used identify performance problem.…”

Global Regulatory Landscape for Aggregate Safety Assessments: Recent Developments and Future Directions

“…To meet the spirit of the final rule, sponsors conducting clinical trials under the authority of the FDA have responded by developing processes and tools to evaluate, assess, and act on accumulating safety information during development on an ongoing basis to ensure earlier identification of safety concerns and to take appropriate steps to mitigate risks. 23 Auspiciously, some multidisciplinary teams (advocated for in CIOMS VI) have been implementing procedures [53][54][55][56][57][58] for review of aggregate blinded clinical trial data to support safety signal detection and risk-management activities, minimizing the need to intentionally unblind data in ongoing studies.…”