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008			180731s2018 gw | s |||| 0|eng d
020			_a9783319927473 _9
024	7		_a10.1007/978-3-319-92747-3 _2doi
040			_aES-MaUEC _bspa
050		4	_aRA409 2018 EB
100	1		_aCleophas, Ton J. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://viaf.org/viaf/5106724 _986087
245	1	0	_aModern Bayesian Statistics in Clinical Research _cby Ton J. Cleophas, Aeilko H. Zwinderman.
264		1	_aCham, Switzerland _bSpringer International Publishing _c2018
300			_a1 recurso en línea (X, 188 páginas 84 ilustraciones, 38 ilustraciones a color)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
505	0		_aPreface -- 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. .
520	3		_aThe 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. .
650		7	_aEstadística médica _2embne _9144772
700	1		_aZwinderman, Aeilko H. _eautor _0http://id.loc.gov/authorities/names/n00010429 _0http://viaf.org/viaf/69166034 _986088
710	2		_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _0http://viaf.org/viaf/148105729 _9106996
776	0	8	_iEdición impresa: _z9783319927466
776	0	8	_iEdición impresa: _z9783319927480
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-92747-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aMedicine (Springer-11650)
988			_aSpringer_Medicine_2018
998			_b01/2019 _dz _ek _feng _ggw _h0
999			_c99853 _d99853 _x1