Ton J. Cleophas, Aeilko H. Zwinderman

Modern Bayesian Statistics in Clinical Research (eBook, PDF)

• Format: PDF

Produktbeschreibung

The current textbook has been written as a help to medical / health professionals and students for the study of modern Bayesian statistics, where posterior and prior odds have been replaced with posterior and prior likelihood distributions. Why may likelihood distributions better than normal distributions estimate uncertainties of statistical test results? Nobody knows for sure, and the use of likelihood distributions instead of normal distributions for the purpose has only just begun, but already everybody is trying and using them. SPSS statistical software version 25 (2017) has started to provide a combined module entitled Bayesian Statistics including almost all of the modern Bayesian tests (Bayesian t-tests, analysis of variance (anova), linear regression, crosstabs etc.).

Modern Bayesian statistics is based on biological likelihoods, and may better fit clinical data than traditional tests based normal distributions do. This is the first edition to systematically implymodern Bayesian statistics in traditional clinical data analysis. This edition also demonstrates that Markov Chain Monte Carlo procedures laid out as Bayesian tests provide more robust correlation coefficients than traditional tests do. It also shows that traditional path statistics are both textually and conceptionally like Bayes theorems, and that structural equations models computed from them are the basis of multistep regressions, as used with causal Bayesian networks.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 188
• Erscheinungstermin: 31. Juli 2018
• Englisch
• ISBN-13: 9783319927473
• Artikelnr.: 53469890

Autorenporträt

The authors are well-qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015), and Professor Cleophas is past-president of the American College of Angiology (2000-2002).

Professor Zwinderman is one of the Principle Investigators of the Academic Medical Center Amsterdam, and his research is concerned with developing statistical methods for new research designs in biomedical science, particularly integrating omics data, like genomics, proteomics, metabolomics, and analysis tools based on parallel computing and the use of cluster computers and grid computing.

Professor Cleophas is a member of the Academic Committee of the European College of Pharmaceutical Medicine, that provides, on behalf of 22 European Universities, the Master-ship trainings "Pharmaceutical Medicine" and "Medicines Development".

From their expertise theyshould be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 18 years, and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.

The authors as professors and teachers in statistics at universities in The Netherlands and France for the most part of their lives, are concerned, that their students find regression-analyses harder than any other methodology in statistics. This is serious, because almost all of the novel methodologies in current data mining and data analysis include elements of regression-analysis, and they do hope that the current production "Regression Analysis for Starters and 2nd Levelers" will be a helpful companion for the purpose.

Five textbookscomplementary to the current production and written by the same authors are

Statistics applied to clinical studies 5th edition, 2012,

Machine learning in medicine a complete overview, 2015,

SPSS for starters and 2nd levelers 2nd edition, 2015,

Clinical data analysis on a pocket calculator 2nd edition, 2016,

Modern Meta-analysis, 2017

Regression Analysis in Medical Research, 2018

all of them published by Springer

Inhaltsangabe

Preface.- General Introduction to Modern Bayesian Statistics.- Traditional Bayes: Diagnostic Tests, Genetic Research, Bayes and Drug Trials.- Bayesian Tests for One Sample Continuous Data.- Bayesian Tests for One Sample Binary Data.- Bayesian Paired T-Tests.- Bayesian Unpaired T-Tests.- Bayesian Regressions.- Bayesian Analysis of Variance (Anova).- Bayesian Loglinear Regression.- Bayesian Poisson Rate Analysis.- Bayesian Pearson Correlations.- Bayesian Statistics: Markov Chain Monte Carlo Sampling.- Bayes and Causal Relationships.- Bayesian Network.- Index.