Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer

Slideshow

Thomas A. Louis

Louis
<a href="http://www.biostat.jhsph.edu/~tlouis/">Johns Hopkins Bloomberg School of Public Health</a>

The use of Bayesian designs and analyses in biomedical and many other applications has burgeoned, even though its use entails additional overhead. Consequently, it is evident that statisticians and collaborators are increasingly finding the approach worth the bother. To help explain this increase in prevalence, I highlight a subset of the potential advantages of the Bayesian formalism and Bayesian philosophy in study design (“Everyone is a Bayesian in the design phase”), conduct, analysis and reporting. Strategic approaches include use of the formalism to develop designs and analyses with required frequentist properties (Bayes for frequentist) and for fully Bayesian goals (Bayes for Bayes). Examples include sample size estimation, use of historical controls, accommodating subgroups, dealing with multiplicity, and addressing complex goals. The Bayesian approach is by no means a panacea. Valid development and application places additional obligations on the investigative team, and so it isn’t always worth the effort. However, the investment can pay big dividends, the cost/benefit relation is increasingly attractive, and in many situations it is definitely worth the bother.

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.