title: Bayeswatch: an overview of Bayesian statistics.
abstract: Increasingly, clinical research is evaluated on the quality of its statistical analysis. Traditionally, statistical analyses in clinical research have been carried out from a 'frequentist' perspective. The presence of an alternative paradigm - the Bayesian paradigm - has been relatively unknown in clinical research until recently. There is currently a growing interest in the use of Bayesian statistics in health care research. This is due both to a growing realization of the limitations of frequentist methods and to the ability of Bayesian methods explicitly to incorporate prior expert knowledge and belief into the analyses. This is in contrast to frequentist methods, where prior experience and beliefs tend to be incorporated into the analyses in an ad hoc fashion. This paper outlines the frequentist and Bayesian paradigms. Acute myocardial infarction mortality data are then analysed from both a Bayesian and a frequentist perspective. In some analyses, the two methods are seen to produce comparable results; in others, they produce different results. It is noted that in this example, there are clinically relevant questions that are more easily addressed from a Bayesian perspective. Finally, areas in clinical research where Bayesian ideas are increasingly common are highlighted.
authors.affiliation: Institute for Clinical Evaluative Sciences, Toronto, Canada. peter.austin@ices.on.ca
authors.forename: Peter C
authors.initials: PC
authors.lastname: Austin
authors.forename: Lawrence J
authors.initials: LJ
authors.lastname: Brunner
authors.forename: Janet E
authors.initials: JE
authors.lastname: Hux
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D009203:Myocardial Infarction / Q000401:mortality