A Statistical Method for Evaluating the Identified Risk in a Risk Management Plan Using the Relative Belief Ratio Based on the Incidence Rate and Data from the Post-marketing Surveillance

Abstract

To ensure the safety of drugs, developing a risk management plan (RMP) is required to manage a series of risks in drug development, including the important identified risks, important potential risks, and important missing information for predicting the post-marketing safety of the drug. Few methods to assess data from a singlearm study conducted as a post-marketing surveillance (PMS) about the magnitude of risks identified before the surveillance, however, have been established. We propose a novel method for evaluating the identified risks using a statistical inference framework based on the relative belief ratio (RB ratio), a ratio of the posterior distribution to the prior distribution. In our context, the RB ratio is defined as the ratio of a prior distribution based on the incidence of adverse drug reactions identified as a rate of risk at the time of RMP development and the number of events calculated from the total person-time of PMS observations to a posterior distribution updated after the PMS data are available. We illustrate our method to apply real data from the published RMP and its PMS report of a drug, and show the usefulness of our method.