Select a topic and related paper from the Bayesian literature. Read “Bayesian Methods in Health Economics” and do the following:
1. Select a topic and related paper (or papers) from the Bayesian literature. The paper(s) should be from an academic or professional journal, in statistics or some related discipline. Preferably the paper(s) will have been published within the last decade or two, though exceptions may be made. Multiple papers may be from the same or from diﬀerent journals.
2. Project report should discuss the topic and paper(s) you selected, along with their importance, relevance, signiﬁcance.
3. Project report should include a relevant Bayesian data analysis. This may be an analysis of a data set found in the paper(s) you selected or an analysis of a diﬀerent data set using the model and methods described in the paper(s). It may simply reproduce/verify an analysis in the paper(s), or it may be something new (i.e. an improvement).
Bayesian analysis refers to a different approach to statistical inference in which the purpose of collecting new data is to refine the estimate of a particular quantity (often a probability) that may be used for decision-making. This is in contrast to traditional ‘frequentist’ statistics where data are collected to reject or confirm a null hypothesis at a given level of statistical significance.
More specifically, Bayesian techniques are used to synthesize information known about a parameter prior to conducting a study with new data from the study to estimate a ‘posterior’ distribution for that parameter. Although the principle of Bayesian inference was first put forward by Rev. Thomas Bayes in the eighteenth century it was not until the powerful computers became widely accessible and new computational methods were developed in the 1980s that application of the technique was widely possible.
In healthcare evaluation Bayesian analysis is most commonly seen in network meta-analysis, and in certain aspects of (adaptive) trial design. It can be argued that much of economic modelling and decision analysis is Bayesian in its approach.