Information‐based group sequential design for post‐market safety monitoring of medical products using real world data

Abstract

Real world healthcare data are commonly used in post‐market safety monitoring studies to address potential safety issues related to newly approved medical products. Such studies typically involve repeated evaluations of accumulating safety data with respect to pre‐defined hypotheses, for which the group sequential design provides a rigorous and flexible statistical framework. A major challenge in designing a group sequential safety monitoring study is the uncertainty associated with product uptake, which makes it difficult to specify the final sample size or maximum duration of the study. To deal with this challenge, we propose an information‐based group sequential design which specifies a target amount of information that would produce adequate power for detecting a clinically significant effect size. At each interim analysis, the variance estimate for the treatment effect of interest is used to compute the current information time, and a pre‐specified alpha spending function is used to determine the stopping boundary. The proposed design can be applied to regression models that adjust for potential confounders and/or heterogeneous treatment exposure. Simulation results demonstrate that the proposed design performs reasonably well in realistic settings