000			05244nam a22005175i 4500
001			978-1-4614-1353-0
003			DE-He213
005			20140220083242.0
007			cr nn 008mamaa
008			120305s2012 xxu| s |||| 0|eng d
020			_a9781461413530 _9978-1-4614-1353-0
024	7		_a10.1007/978-1-4614-1353-0 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMBNS _2bicssc
072		7	_aMED090000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aVittinghoff, Eric. _eauthor.
245	1	0	_aRegression Methods in Biostatistics _h[electronic resource] : _bLinear, Logistic, Survival, and Repeated Measures Models / _cby Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch.
250			_a2nd ed. 2012.
264		1	_aBoston, MA : _bSpringer US, _c2012.
300			_aXX, 509p. 65 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStatistics for Biology and Health, _x1431-8776
505	0		_aIntroduction -- Exploratory and Descriptive Methods -- Basic Statistical Methods -- Linear Regression -- Logistic Regression -- Survival Analysis -- Repeated Measures Analysis -- Generalized Linear Models -- Strengthening Casual Inference -- Predictor Selection -- Complex Surveys -- Summary.
520			_aThis new edition 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. In the second edition, the authors have substantially expanded the core chapters, including new coverage of exact, ordinal, and multinomial logistic models, discrete time and competing risks survival models, within and between effects in longitudinal models, zero-inflated Poisson and negative binomial models, cross-validation for prediction model selection, directed acyclic graphs, and sample size, power and minimum detectable effect calculations; Stata code is also updated. In addition, there are new chapters on methods for strengthening causal inference, including propensity scores, marginal structural models, and instrumental variables, and on methods for handling missing data, using maximum likelihood, multiple imputation, inverse weighting, and pattern mixture models. From the reviews of the first edition: "This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered." Journal of Biopharmaceutical Statistics, 2005 "This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book" Technometrics, February 2006 "Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally." Journal of the American Statistical Association, March 2006
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		_aGlidden, David V. _eauthor.
700	1		_aShiboski, Stephen C. _eauthor.
700	1		_aMcCulloch, Charles E. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461413523
830		0	_aStatistics for Biology and Health, _x1431-8776
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-1353-0
912			_aZDB-2-SMA
999			_c101054 _d101054