Sammendrag
Incorporating external information, such as prior information and expert opinions, can play an important role in the design, analysis and interpretation of clinical trials. Seeking effective schemes for incorporating prior information with the primary outcomes of interest has drawn increasing attention in pharmaceutical applications in recent years. Most methods currently used for combining prior information with clinical trial data are Bayesian. We demonstrate that they sometimes may encounter a counterintuitive 'outlying posterior' phenomenon in the analysis of clinical trials with binary outcomes, especially when informative prior distribution is skewed.
In this talk, we present an alternative frequentist framework of combining information using confidence distributions (CDs), and illustrate it through an example of incorporating expert opinions with information from clinical trial data. A CD uses a distribution function to estimate a parameter of interest. Some researchers have suggested that a CD is a 'frequentist analogue of a Bayesian posterior', although the notion of CD, especially in its asymptotic form, is much broader. In this talk, we present a formal definition of CDs, and develop a general framework of combining information based on CDs. This CD combining framework not only unifies most existing meta-analysis approaches, but also leads to development of new approaches. In particular, we develop a frequentist approach to combine surveys of expert opinions with binomial clinical trial data, and illustrate it using data from a collaborative research with Johnson & Johnson Pharmaceuticals. The results from the frequentist approach are compared with those from Bayesian approaches.