Robust safety monitoring and signal detection using alternatives to the standard poisson distribution.

Abstract

Proper and timely characterization of the safety profile of a pharmaceutical product under development is imperative for assessing the overall benefit-risk relationship of the product and for making key development decisions. For ongoing clinical development, a comprehensive and robust safety monitoring and safety signal detection program which is based upon quantitative statistical reasoning is critical. Methods presented here can be applied to safety signal detection and periodic safety monitoring. Various statistical properties, distributions, and models, all utilizing a Bayesian framework are considered and further examined in order to identify robust methods applicable to a broad set of scenarios and situations. Methods developed for incidence counts (including those with under-dispersed distributions) with variable time-at-risk and with underlying constant or non-constant hazard rates, are proposed and compared to traditional methods designed to assess adverse event incidence rates or binomial incidence proportions (which assume an underlying constant hazard rate and subsequent Poisson distribution for modeling event counts).