Multivariable analysis : a practical guide for clinicians and public health researchers / Mitchell H. Katz
- 作者： Katz, Mitchell H. 1959-
- 出版： Cambridge ;New York : Cambridge University Press 2011
- 叢書名： Cambridge medicine
- 主題： Medicine--Research--Statistical methods. , Multivariate analysis. , Biometry. , Medical statistics. , Multivariate analysis. , Biometry--methods
- 版本：3rd ed.
- ISBN： 9780521141079 (pbk.) :: US$66.00 、 0521141079 (pbk.) 、 9780521760980 (hbk.) 、 0521760984 (hbk.)
- 一般註：Includes bibliographical references and index
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讀者標籤：
- 系統號： 005206829 | 機讀編目格式
館藏資訊
摘要註
"Now in its third edition, this highly successful text has been fully revised and updated with expanded sections on cutting-edge techniques including Poisson regression, negative binomial regression, multinomial logistic regression and proportional odds regression. As before, it focuses on easy-to-follow explanations of complicated multivariable techniques. It is the perfect introduction for all clinical researchers. It describes how to perform and interpret multivariable analysis, using plain language rather than complex derivations and mathematical formulae. It focuses on the nuts and bolts of performing research, and prepares the reader to set up, perform and interpret multivariable models. Numerous tables, graphs and tips help to demystify the process of performing multivariable analysis. The text is illustrated with many up-to-date examples from the medical literature on how to use multivariable analysis in clinical practice and in research"--Provided by publisher
內容註
Common uses of multivariable models -- Outcome variables in multivariable analysis -- Independent variables in multivariable analysis -- Relationship of independent variables to one another -- Setting up a multivariable analysis -- Performing the analysis -- Interpreting the results -- Delving deeper: checking the underlying assumptions of the analysis -- Propensity scores -- Correlated observations -- Validation of models -- Special topics -- Publishing your study -- Summary: steps for constructing a multivariable model