Why Bayesian analysis hasn't caught on in healthcare decision making.
The objective of this paper is to discuss why Bayesian statistics is not used more in healthcare decision making and what might be done to increase the use of Bayesian methods. First, a case is made for why Bayesian analysis should be used more widely. Serious weaknesses of commonly used frequentist methods are discussed and contrasted with advantages of Bayesian methods. Next, the question of why Bayesian methods are not used more widely is addressed, considering both philosophical differences and practical issues. Contrary to what some might think, the practical issues are more important in this regard. Finally, some steps to encourage increased use of Bayesian methods in healthcare decision making are presented and discussed. These ideas are straightforward but are by no means trivial to implement, largely because it is difficult to fight tradition and make major paradigm shifts quickly. The primary needs are improved Bayesian training at the basic level (which means textbooks and other materials as well as training of those who teach at the basic level), procedures to make Bayesian analysis easier to understand and use (better software and standard methods for displaying and communicating Bayesian outputs will help here), and the education of decision makers about the advantages of Bayesian methods in important healthcare decision-making problems.
Winkler
R L
RL
Duke University, USA.
D001499:Bayes Theorem; D003659:Decision Making, Organizational; D004059:Diffusion of Innovation; D004493:Education; D005161:Facility Regulation and Control; D006298:Health Services Administration; D012107:Research Design; D014481:United States