Author: Eric Vittinghoff
Edition:
Publisher: Springer
Binding: Paperback
ISBN: 1441919058
Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health)
Here is a unified, readable introduction to multipredictor regression methods in biostatistics, including linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, and generalized linear models for counts and other outcomes. Medical books Regression Methods in Biostatistics. Medical books Regression Methods in Biostatistics. This 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.
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This 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
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
Here is a unified, readable introduction to multipredictor regression methods in biostatistics, including linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, and generalized linear models for counts and other outcomes. The authors describe shared elements in methods for selecting, estimating, checking, and interpreting each model, and show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
"Here is a unified, readable introduction to multipredictor regression methods in biostatistics, including linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, and generalized linear models for counts and other outcomes. The authors describe shared elements in methods for selecting, estimating, checking, and interpreting each model, and show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.File Size: 4905 KBPrint Length: 360 pagesPublisher: Spring
Springer. Hardcover. 0387202757 Hardcover; 2007 Springer; 344 pages; "Regression Methods in Biostatistics: Linear Logistic Survival and Repeated Measures Models Statistics for Biology and Health" by Eric Vittinghoff et al. . Very Good.
Medical Book Regression Methods in Biostatistics
The authors describe shared elements in methods for selecting, estimating, checking, and interpreting each model, and show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
