000			03811nam a22004695i 4500
001			978-0-387-27255-9
003			DE-He213
005			20250710083938.0
007			cr nn 008mamaa
008			100301s2005 xxu| s |||| 0|eng d
020			_a9780387272559 _a99780387272559
024	7		_a10.1007/b138825 _2doi
082	0	4	_a519.5 _223
100	1		_aVittinghoff, Eric. _eauthor.
245	1	0	_aRegression Methods in Biostatistics _h[recurso electrónico] : _bLinear, Logistic, Survival, and Repeated Measures Models / _cby Eric Vittinghoff, Stephen C. Shiboski, David V. Glidden, Charles E. McCulloch.
264		1	_aNew York, NY : _bSpringer New York, _c2005.
300			_aXV, 340 p. 54 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStatistics for Biology and Health, _x1431-8776
505	0		_aExploratory and Descriptive Methods -- Basic Statistical Methods -- Linear Regression -- Predictor Selection -- Logistic Regression -- Survival Analysis -- Repeated Measures and Longitudinal Data Analysis -- Generalized Linear Models -- Complex Surveys -- Summary.
520			_aThis new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses. The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
650		0	_aSTATISTICS.
650		0	_aEPIDEMIOLOGY.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICS FOR LIFE SCIENCES, MEDICINE, HEALTH SCIENCES.
650	2	4	_aEPIDEMIOLOGY.
650	2	4	_aPUBLIC HEALTH/GESUNDHEITSWESEN.
700	1		_aShiboski, Stephen C. _eauthor.
700	1		_aGlidden, David V. _eauthor.
700	1		_aMcCulloch, Charles E. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387202754
830		0	_aStatistics for Biology and Health, _x1431-8776
856	4	0	_uhttp://dx.doi.org/10.1007/b138825 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c56647 _d56647