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Modern Bayesian statistics in clinical research / Ton J. Cleophas, Aeilko H. Zeinderman.

Cleophas, Ton J. M.; Zwinderman, Aeilko H.
2018
QA279.5
Available Online
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Title
Modern Bayesian statistics in clinical research / Ton J. Cleophas, Aeilko H. Zeinderman.
Author
Cleophas, Ton J. M.
ISBN
9783319927473 (electronic book)
3319927477 (electronic book)
9783319927466
3319927469
DOI
https://doi.org/10.1007/978-3-319-92747-3
Publication Details
Cham : Springer, 2018.
Language
English
Description
1 online resource.
Item Number
10.1007/978-3-319-92747-3 doi
Call Number
QA279.5
Dewey Decimal Classification
519.5/42
Summary
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 imply modern 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. .
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Description based on print version record.
Added Author
Zwinderman, Aeilko H.
Available in Other Form
Print version: 9783319927466
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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.

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