Strategies on Using Prior Information When Assessing Adverse Events

Abstract

Assessing safety in drug development naturally incorporates an accumulation of knowledge as we progress from one study to another in a clinical development program. Ideally, it is the early clinical trial data that give us the greatest opportunity to leverage relevant historical information into the design and analysis of later phase clinical trials. While Bayesian methods would appear to provide an ideal framework for assessing safety in this context, concerns regarding the formulation and prespecification of a prior have limited their use in practice. More specifically, when information from previous studies is used to form a prior, an implicit assumption of exchangeability is made. However, the possibility of nonexchangeability, which could lead to conflict between prior and the data, cannot be ruled out. Based on these challenges, in this article we outline a number of strategies based on using simple Bayesian methods to assess safety concerns related to a prespecified adverse event. Three approaches to forming a prior distribution will be examined: (i) a single informative conjugate prior; (ii) a meta-analytic-predictive prior (MAP), which comprises of a mixture of conjugate priors; and (iii) a robust mixture prior, involving a combination of either the single conjugate prior or MAP with a noninformative prior. In the final case, when prior-data conflict arises between the historical informative prior and the data collected from the concurrent study, the addition of a noninformative prior serves as an automatic corrective feature. These methods will be illustrated with a motivating example involving the development of a new drug/delivery device for the treatment of agitation in schizophrenia. In addition, a simulation study will examine the performance of each approach to prior specification.