Safety Data Analysis
Safety data have traditionally been summarized as tables and listings that are difficult to review and interpret. In today’s climate of heightened safety concerns, these formats are inadequate for quick and accurate interpretation and comprehension. Safety Miner provides methods for the rigorous analysis of safety data. Analysis results and reports can be readily deployed for interactive review and for submission / reporting.
Safety data pose many challenges to the analyst; there are many endpoints (labs/analytes and adverse events) and comparisons to make, resulting in a testing multiplicity issue. Further, AE count data are sparse, thus complicating the analysis. Safety Miner packages include advanced statistical methods such as Bayesian hierarchical models and inside-out machine learning models that directly address the peculiar safety data issues. These methods provide accurate statistical inference and rapid identification of safety signals.
Improve analysis of safety data and identify safety issues faster
Deploy analytic best practices across functional areas
Align with regulatory and company mandates