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Table of contents:
Blackwell Publishing is delighted to announce that this book has been Highly Commended in the 2004 BMA Medical Book Competition. Here is the judges' summary of this book: This is a technical book on a technical subject but presented in a delightful way. There are many books on statistics for doctors but there are few that are excellent and this is certainly one of them. Statistics is not an easy subject to teach or write about. The authors have succeeded in producing a book that is as good as it can get. For the keen student who does not want a book for mathematicians, this is an excellent first book on medical statistics. Essential Medical Statistics is a classic amongst medical statisticians. An introductory textbook, it presents statistics with a clarity and logic that demystifies the subject, while providing a comprehensive coverage of advanced as well as basic methods. The second edition of Essential Medical Statistics has been comprehensively revised and updated to include modern statistical methods and modern approaches to statistical analysis, while retaining the approachable and non-mathematical style of the first edition. The book now includes full coverage of the most commonly used regression models, multiple linear regression, logistic regression, Poisson regression and Cox regression, as well as a chapter on general issues in regression modelling.In addition, new chapters introduce more advanced topics such as meta-analysis, likelihood, bootstrapping and robust standard errors, and analysis of clustered data. Aimed at students of medical statistics, medical researchers, public health practitioners and practising clinicians using statistics in their daily work, the book is designed as both a teaching and a reference text. The format of the book is clear with highlighted formulae and worked examples, so that all concepts are presented in a simple, practical and easy-to-understand way. The second edition enhances the emphasis on choice of appropriate methods with new chapters on strategies for analysis and measures of association and impact. Essential Medical Statistics is supported by a web site. This useful online resource provides statistical datasets to download, as well as sample chapters and future updates.
Contents:
Part A
Basics 1
Using this book 2
Defining the data 3
Displaying the data Part B
Analysis of numerical outcomes 4
Means, Standard Deviations and Standard Errors 5
The Normal Distribution 6
Confidence Interval for a Mean 7
Comparison of two means: confidence intervals, hypothesis tests and P-values 8
Using P-values and confidence intervals to interpret the results of statistical analyses 9
Comparison of means from several groups: analysis of variance 10
Linear Regression and Correlation 11
Multiple Regression 12
Goodness of fit and regression diagnostics 13
Transformations Part C
Analysis of binary outcomes 14
Probability, risks and odds (of disease) 15
Proportions and the binomial distribution 16
Comparing two proportions 17
Chi-squared tests for 2 2 and larger contingency tables 18
Controlling for confounding: stratification 19
Logistic regression: comparing two or more exposure groups 20
Logisitic regression: controlling for confounding and other extensions 21
Matched studies Part D
Longitudinal studies: Analysis of rates and survival times 22
Longitudinal studies, rates and the Poisson distribution 23
Comparing rates 24
Poisson regression 25
Standardisation 26
Survival analysis: displaying and comparing survival patterns 27
Regression analysis of survival data Part E
Statistical modelling 28
Likelihood 29
Regression modelling 30
Relaxing model assumptions 31
Analysis of clustered data 32
Systematic reviews and meta-analysis 33
Bayesian statistics Part F
Study design, analysis and interpretation 34
Linking analysis to study design: summary of methods 35
Calculation of Required Sample Size 36
Measurement error: assessment and implications 37
Measures of association and impact 38
Strategies for analysis APPENDIX: Statistical Tables Bibliography
Brief Description:
Includes modern statistical methods and modern approaches to statistical analysis. This book also includes coverage of the most commonly used regression models, multiple linear regression, logistic regression, Poisson regression and Cox regression, as well as a chapter on general issues in regression modelling.
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