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008			150903s2005 xxu| o |||| 0|eng d
020			_a9780387272559 _9978-0-387-27255-9
024	7		_a10.1007/b138825 _2doi
035			_avtls000330367
039		9	_a201509030400 _bVLOAD _c201405070514 _dVLOAD _c201401311338 _dstaff _c201401311202 _dstaff _y201401291453 _zstaff _wmsplit0.mrc _x787
050		4	_aQA276-280
100	1		_aVittinghoff, Eric. _eautor _9302405
245	1	0	_aRegression Methods in Biostatistics : _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 páginas, 54 illus. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aStatistics for Biology and Health, _x1431-8776
500			_aSpringer eBooks
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).
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aShiboski, Stephen C. _eautor _9302406
700	1		_aGlidden, David V. _eautor _9302407
700	1		_aMcCulloch, Charles E. _eautor _9302408
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387202754
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b138825 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c278647 _d278647