Statistical modeling for biomedical researchers : a simple introduction to the analysis of complex data

Select an Action

Title:

Statistical modeling for biomedical researchers : a simple introduction to the analysis of complex data

Author:

Dupont, William D., 1946-

ISBN:

9780521655781

9780521820615

Personal Author:

Dupont, William D., 1946-

Publication Information:

Cambridge, UK ; New York : Cambridge University Press, cop. 2002.

Physical Description:

XVII, 386 str. : ilustr. ; 25 cm.

Contents:

Ch. 1. Introduction -- Ch. 2. Simple linear regression -- Ch. 3. Multiple linear regression -- Ch. 4. Simple logistic regression -- Ch. 5. Multiple logistic regression -- Ch. 6. Introduction to survival analysis -- Ch. 7. Hazard regression analysis -- Ch. 8. Introduction to poisson regression: inferences on morbidity and mortality rates -- Ch. 9. Multiple poisson regression -- Ch. 10. Fixed effects analysis of variance -- Ch. 11. Repeated-measures analysis of variance.

Subject Term:

Medicine -- Research -- Statistical methods -- Mathematical models.

Biometry -- methods -- Problems and Exercises.

Models, Statistical -- Problems and Exercises.

Data Interpretation, Statistical -- Problems and Exercises.

Mathematical Computing -- Problems and Exercises.

Copies:

Available:*

Library	Material Type	Item Barcode	Shelf Number	Copy	Status
Searching... 1:IIEMSA	1:GEN-BOOK	33168025464666	610.727 D896S 2002	1	Searching... Unknown
Searching... 1:IIEMSA	1:GEN-BOOK	33168025460441	610.727 D896S 2002	2	Searching... Unknown

Bound With These Titles

On Order

Summary

This text will enable biomedical researchers to use a number of advanced statistical methods that have proven valuable in medical research. It is intended for people who have had an introductory course in biostatistics. A statistical software package (Stata) is used to avoid mathematics beyond the high school level. The emphasis is on understanding the assumptions underlying each method, using exploratory techniques to determine the most appropriate method, and presenting results in a way that will be readily understood by clinical colleagues. Numerous real examples from the medical literature are used to illustrate these techniques. Graphical methods are used extensively. Topics covered include linear regression, logistic regression, Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measures analysis of variance. Each method is introduced in its simplest form and is then extended to cover situations in which multiple explanatory variables are collected on each study subject.