Statistical approaches to group sequential monitoring of postmarket safety surveillance data: current state of the art for use in the Mini‐Sentinel pilot

Abstract

Purpose This manuscript describes the current statistical methodology available for active postmarket surveillance of pre-specified safety outcomes using a prospective incident user concurrent control cohort design with existing electronic healthcare data. Methods Motivation of the active postmarket surveillance setting is provided using the Food and Drug Administration's Mini-Sentinel Pilot as an example. Four sequential monitoring statistical methods are presented including the Lan-Demets error spending approach, a matched likelihood ratio test statistic approach with the binomial MaxSPRT as a special case, the conditional sequential sampling procedure with stratification, and a generalized estimating equation regression approach using permutation. Information on the assumptions, limitations, and advantages of each approach is provided, including how each method defines sequential monitoring boundaries, what test statistic is used, and how robust it is to settings of rare events or frequent testing. Results A hypothetical example of how the approaches could be applied to data comparing a medical product of interest, drug A, to a concurrent control drug, drug B, is presented including providing the type of information one would have available for monitoring such drugs. Summary We have described the current state of methodology for postmarket surveillance of pre-specified safety outcomes. We describe the limitations and advantages of the approaches while acknowledging areas for future development.

A Comparison of Active Adverse Event Surveillance Systems Worldwide

“…Maximized sequential probability ratio testing (maxSPRT) is one of the commonly used techniques and has been used in the VRMM’s (UK) enhanced proactive pharmacovigilance, as well as by the Mini-Sentinel and the VSD (USA) [39]. MaxSPRT supports continuous or time-period analyses as data are collected, and allows detection of a change in the probabilities of ADEs after the introduction of a new vaccine or drug [38].…”

Common data model for decision support system of adverse drug reaction to extract knowledge from multi-center database

“…The Mini-Sentinel project used a distributed database for organizing and standardizing databases. The standardized data at the individual level remained in the local database [15]. The Mini-Sentinel program used claims data and outpatient and inpatient EMR and registries.…”

Sequential surveillance for drug safety in a regulatory environment

“…As experience with sequential surveillance grows and data sources improve, simulations and practical applications will likely be necessary to address key questions (Table ) related to the scope of surveillance activities, the specification of hypotheses, the challenge of dynamic distributed data, epidemiologic design, and sensitivity analyses. Trade‐offs in statistical power and timeliness related to signaling thresholds will need to be further examined, and effects on regulatory decision making will also need to be modeled . Progress in sequential surveillance will likely lead to a range of options rather than a single approach since the question being asked influences both the decision to initiate sequential surveillance and the characteristics of a sequential surveillance program.…”