Survival Analysis for Epidemiologic and Medical Research

Front Cover
Cambridge University Press, Mar 3, 2008 - Medical
This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in survival analysis concepts and techniques for non-statisticians.
 

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Contents

Section 1
21
Section 2
27
Section 3
37
Section 4
39
Section 5
53
Section 6
64
Section 7
68
Section 8
71
Section 11
111
Section 12
112
Section 13
119
Section 14
129
Section 15
155
Section 16
167
Section 17
169
Section 18
184

Section 9
90
Section 10
102
Section 19
208

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About the author (2008)

Steve Selvin is Professor of Biostatistics and Epidemiology at the University of California, Berkeley. He has taught on the Berkeley campus for 35 years and has authored or co-authored more than 200 scientific articles in the areas of applied statistics and epidemiology. He has received two university teaching awards and is a member of the ASPH/Pfizer Public Health Academy of Distinguished Teachers.

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