Explore the forefront of statistical analysis with "Modern Bayesian Statistics in Clinical Research." This first edition hardcover book, authored by Ton J. Cleophas and Aeilko H. Zwinderman, systematically applies modern Bayesian statistics to traditional clinical data analysis. Discover how likelihood distributions can offer superior uncertainty estimates compared to normal distributions, a feature now integrated into SPSS statistical software. This edition showcases the robustness of Markov Chain Monte Carlo procedures in Bayesian tests for correlation coefficients and explores the conceptual similarities between traditional path statistics and Bayes theorems, forming the basis of multistep regressions in causal Bayesian networks. Key Features: * Systematic application of modern Bayesian statistics to clinical data. * Utilizes likelihood distributions for more accurate uncertainty estimation. * Demonstrates the robustness of Bayesian tests using Markov Chain Monte Carlo. * Explores the relationship between traditional path statistics and Bayes theorems. * Covers Bayesian t-tests, ANOVA, linear regression, and crosstabs. This comprehensive guide is ideal for medical/health professionals and students seeking to understand and apply advanced statistical methods in their research. Summary: Modern Bayesian Statistics in Clinical Research, Springer International Publishing AG, Hardcover, English, First edition, 188 pages, ISBN 9783319927466, written by Ton J. Cleophas and Aeilko H. Zwinderman.