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    Bayesian Thinking in Biostatistics (2021)

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      Bayesian statistical methods have found increased use and acceptance in biomedical research. More and more clinical trials are including Bayesian considerations in their designs. Interim analyses based on posterior or predictive probabilities have appeared in protocols for clinical studies sponsored by the largest pharmaceutical companies and at internationally renowned academic medical centers. Governmental regulatory agencies, such as the U.S. Food and Drug Administration and the European Medicines Agency, are becoming more open to considering clinical trials with Bayesian designs. In our roles as biostatisticians at active academic medical centers, we have used Bayesian methods in our collaborations. We receive frequent requests for information about Bayesian methods from our colleagues in other disciplines. We teach courses on Bayesian methods 1 focusing on biostatistical applications 1 and our students come from many different departments. Given this widespread interestespecially from our colleagues in disciplines outside of statistics-we felt the time is right for an introductory textbook that reviews Bayesian inference from the standpoint of someone whose interest is biostatistical but whose background is not. Aside from providing a summary of the concepts and theory of Bayesian inference, information that one can find in many available texts, we also discuss methods that biomedical investigators would want to use for their research. We cover Bayesian methods for analyzing time-to-event data (i.e., survival analysis), clinical trial design, longitudinal data analysis, and diagnostic tests.


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