Login to MyKarger

New to MyKarger? Click here to sign up.



Login with Facebook

Forgot your password?

Authors, Editors, Reviewers

For Manuscript Submission, Check or Review Login please go to Submission Websites List.

Submission Websites List

Institutional Login
(Shibboleth or Open Athens)

For the academic login, please select your country in the dropdown list. You will be redirected to verify your credentials.

Cover

Using and Understanding Medical Statistics

5th, revised and extended edition

Matthews D.E. (Waterloo, Ont.) 
Farewell V.T. (Cambridge) 

Status: available   
Publication year: 2015
Buy this book
Print Version: CHF 49.00, EUR 46.00, USD 54.00
The final prices may differ from the prices shown due to specifics of VAT rules, postage and handling.
Order this title

You already have online access to this title. If you would like to buy a personal digital or print copy, please click here.


An excellent introduction and invaluable reference for every medical researcher
The fifth revised edition of this highly successful book presents the most extensive enhancement since Using and Understanding Medical Statistics was first published 30 years ago. Without question, the single greatest change has been the inclusion of source code, together with selected output, for the award-winning, open-source, statistical package known as R. This innovation has enabled the authors to de-emphasize formulae and calculations, and let software do all of the ‘heavy lifting’.
This edition also introduces readers to several graphical statistical tools, such as Q-Q plots to check normality, residual plots for multiple regression models, funnel plots to detect publication bias in a meta-analysis and Bland-Altman plots for assessing agreement in clinical measurements. New examples that better serve the expository goals have been added to a half-dozen chapters. In addition, there are new sections describing exact confidence bands for the Kaplan-Meier estimator, as well as negative binomial and zero-inflated Poisson regression models for over-dispersed count data.
The end result is not only an excellent introduction to medical statistics, but also an invaluable reference for every discerning reader of medical research literature.