title: Using prior distributions to synthesize historical evidence: comments on the Goodman-Sladky case study of IVIg in Guillain-Barré syndrome.
abstract: One feature of the Bayesian approach is that it provides methods for synthesizing what is known about a question of interest and provides a formalism based on the laws of probability for incorporating this auxiliary knowledge into the planning and the analysis of the next study. In this comment, we use elements of the Goodman-Sladky case study to illustrate (1) the use of Bayesian methods to quantify historical information about an intervention through the specification of a prior distribution, (2) an approach to the analysis of the sensitivity of the conclusions of a Bayesian analysis to the specification of the prior distribution, and (3) we comment on the role of research synthesis for combining information about an intervention from different data sources as a tool to help summarize evidence about the intervention.
authors.affiliation: Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA. joel@stat.cmu.edu
authors.forename: Joel B
authors.initials: JB
authors.lastname: Greenhouse
authors.forename: Howard
authors.initials: H
authors.lastname: Seltman
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D002648:Child; D019317:Evidence-Based Medicine / Q000379:methods*; D020275:Guillain-Barre Syndrome / Q000188:drug therapy*; D006801:Humans; D016756:Immunoglobulins, Intravenous / Q000627:therapeutic use*; D016032:Randomized Controlled Trials as Topic / Q000379:methods*
chemical_list: D016756:Immunoglobulins, Intravenous