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.
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.

Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models
509
Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models
509Hardcover(2nd ed. 2012)
Product Details
ISBN-13: | 9781461413523 |
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Publisher: | Springer New York |
Publication date: | 09/01/2011 |
Series: | Statistics for Biology and Health |
Edition description: | 2nd ed. 2012 |
Pages: | 509 |
Product dimensions: | 6.30(w) x 9.40(h) x 1.40(d) |