Modern Bayesian Statistics in Clinical Research

The textbook is written for medical/health professionals and students to study modern Bayesian statistics, which uses posterior and prior likelihood distributions to estimate uncertainties of statistical test results. It suggests that likelihood distributions may better fit clinical data than normal distributions, and that Markov Chain Monte Carlo procedures provide more robust correlation coefficients than traditional tests. It also demonstrates that traditional path statistics are like Bayes theorems and that structural equations models are the basis of multistep regressions with causal Bayesian networks.

\n Format: Hardback
\n Length: 188 pages
\n Publication date: 11 August 2018
\n Publisher: Springer International Publishing AG
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The current textbook, designed to aid medical and health professionals and students in the study of modern Bayesian statistics, has undergone a significant transformation. In this revised edition, the traditional concepts of posterior and prior odds have been replaced with posterior and prior likelihood distributions. The question of why likelihood distributions may outperform normal distributions in estimating uncertainties of statistical test results remains largely unanswered, and the widespread adoption of likelihood distributions in this context is still in its early stages. However, there is a growing enthusiasm among researchers and practitioners to explore and utilize these distributions.

SPSS statistical software version 25 (2017), a widely used tool for statistical analysis, has taken a significant step forward by introducing a dedicated module titled Bayesian Statistics. This comprehensive module encompasses a wide range of modern Bayesian tests, including Bayesian t-tests, analysis of variance (ANOVA), linear regression, and crosstabs. By incorporating modern Bayesian statistics into traditional clinical data analysis, this edition aims to provide a more effective and robust approach to statistical inference.

One of the key advantages of likelihood distributions is their ability to better fit clinical data than traditional tests based on normal distributions. Modern Bayesian statistics is rooted in biological likelihoods, which have been shown to provide a more accurate representation of the underlying data distribution. This alignment can lead to improved statistical power and accuracy in clinical trials and other medical research endeavors.

Furthermore, this edition demonstrates that Markov Chain Monte Carlo (MCMC) procedures, commonly used as Bayesian tests, offer more robust correlation coefficients compared to traditional tests. MCMC is a computational method that generates a series of random samples to estimate the parameters of a probability distribution. By leveraging MCMC, researchers can obtain more reliable estimates of correlation coefficients and other statistical parameters, which can have significant implications for clinical practice and research.

Traditional path statistics, often associated with Bayes theorems, are also explored in this edition. It is shown that these statistics share similar conceptual frameworks and can be used to derive multistep regressions, commonly employed with causal Bayesian networks. Causal Bayesian networks are a powerful tool for modeling and analyzing complex systems, particularly in the field of medicine. By understanding the relationship between variables and their effects on outcomes, researchers can develop more accurate models and make informed decisions about patient care.

In conclusion, the current textbook has been extensively revised to incorporate modern Bayesian statistics and likelihood distributions. This transformation offers a promising approach to statistical inference, particularly in the medical and health fields. By leveraging the power of biological likelihoods, MCMC procedures, and traditional path statistics, researchers and practitioners can gain a deeper understanding of clinical data and make more informed decisions based on evidence. As the adoption of likelihood distributions continues to grow, it is likely to have a significant impact on the field of statistics and clinical research in the years to come.

\n Weight: 426g\n
Dimension: 164 x 242 x 20 (mm)\n
ISBN-13: 9783319927466\n
Edition number: 1st ed. 2018\n