Statistics At Square Two

STATISTICS AT SQUARE TWO
 
An easy-to-follow exploration of intermediate statistical techniques used in medical research
 
In the newly revised third edition of Statistics at Square Two: Understanding Modern Statistical Applications in Medicine, a team of distinguished statisticians delivers an accessible and intuitive discussion of advanced statistical methods for readers and users of scientific medical literature. This will allow readers to engage critically with modern research as the authors explain the correct interpretation of results in the medical literature.
 
The book includes two brand new chapters covering meta-analysis and time-series analysis as well as new references to the many checklists that have appeared in recent years to enable better reporting of contemporary research. Most examples have been updated as well, and each chapter contains practice exercises and answers. Readers will also find sample code (in R) for many of the analyses, in addition to:
* A thorough introduction to models and data, including the different types of data, statistical models, and computer-intensive methods
* Comprehensive explorations of multiple linear regression, including the interpretation of computer output, diagnostic statistics such as influential points, and many uses of multiple regression
* Practical discussions of multiple logistic regression, survival analysis, Poisson regression and random effects models including their uses, examples in the medical literature, and strategies for interpreting computer output
 
Perfect for anyone hoping to better understand the statistics presented in contemporary medical research, Statistics at Square Two: Understanding Modern Statistical Applications in Medicine will also benefit postgraduate students studying statistics and medicine.

List of contents

Preface xi
 
1 Models, Tests and Data 1
 
1.1 Types of Data 1
 
1.2 Confounding, Mediation and Effect Modification 2
 
1.3 Causal Inference 3
 
1.4 Statistical Models 5
 
1.5 Results of Fitting Models 6
 
1.6 Significance Tests 7
 
1.7 Confidence Intervals 8
 
1.8 Statistical Tests Using Models 8
 
1.9 Many Variables 9
 
1.10 Model Fitting and Analysis: Exploratory and Confirmatory Analyses 10
 
1.11 Computer-intensive Methods 11
 
1.12 Missing Values 11
 
1.13 Bayesian Methods 12
 
1.14 Causal Modelling 12
 
1.15 Reporting Statistical Results in the Medical Literature 14
 
1.16 Reading Statistics in the Medical Literature 14
 
2 Multiple Linear Regression 17
 
2.1 The Model 17
 
2.2 Uses of Multiple Regression 18
 
2.3 Two Independent Variables 18
 
2.3.1 One Continuous and One Binary Independent Variable 19
 
2.3.2 Two Continuous Independent Variables 22
 
2.3.3 Categorical Independent Variables 22
 
2.4 Interpreting a Computer Output 23
 
2.4.1 One Continuous Variable 24
 
2.4.2 One Continuous Variable and One Binary Independent Variable 25
 
2.4.3 One Continuous Variable and One Binary Independent Variable with Their Interaction 26
 
2.4.4 Two Independent Variables: Both Continuous 27
 
2.4.5 Categorical Independent Variables 29
 
2.5 Examples in the Medical Literature 31
 
2.5.1 Analysis of Covariance: One Binary and One Continuous Independent Variable 31
 
2.5.2 Two Continuous Independent Variables 32
 
2.6 Assumptions Underlying the Models 32
 
2.7 Model Sensitivity 33
 
2.7.1 Residuals, Leverage and Influence 33
 
2.7.2 Computer Analysis: Model Checking and Sensitivity 34
 
2.8 Stepwise Regression 35
 
2.9 Reporting the Results of a Multiple Regression 36
 
2.10 Reading about the Results of a Multiple Regression 36
 
2.11 Frequently Asked Questions 37
 
2.12 Exercises: Reading the Literature 38
 
3 Multiple Logistic Regression 41
 
3.1 Quick Revision 41
 
3.2 The Model 42
 
3.2.1 Categorical Covariates 44
 
3.3 Model Checking 44
 
3.3.1 Lack of Fit 45
 
3.3.2 "Extra-binomial" Variation or "Over Dispersion" 45
 
3.3.3 The Logistic Transform is Inappropriate 46
 
3.4 Uses of Logistic Regression 46
 
3.5 Interpreting a Computer Output 47
 
3.5.1 One Binary Independent Variable 47
 
3.5.2 Two Binary Independent Variables 51
 
3.5.3 Two Continuous Independent Variables 53
 
3.6 Examples in the Medical Literature 54
 
3.6.1 Comment 55
 
3.7 Case-control Studies 56
 
3.8 Interpreting Computer Output: Unmatched Case-control Study 56
 
3.9 Matched Case-control Studies 58
 
3.10 Interpreting Computer Output: Matched Case-control Study 58
 
3.11 Example of Conditional Logistic Regression in the Medical Literature 60
 
3.11.1 Comment 60
 
3.12 Alternatives to Logistic Regression 61
 
3.13 Reporting the Results of Logistic Regression 61
 
3.14 Reading about the Results of Logistic Regression 61
 
3.15 Frequently Asked Questions 62
 
3.16 Exercise 62
 
4 Survival Analysis 65
 
4.1 Introduction 65
 
4.2 The Model 66
 
4.3 Uses of Cox Regression 68
 
4.4 Interpreting a Computer Output 68
 
4.5 Interpretation of the Model 70
 
4.6 Generalisations of the Model 70
 
4.6.1 Stratified Models 70
 
4.6.2 Time D

About the author

Michael J. Campbell is Emeritus Professor of Medical Statistics at the University of Sheffield in the United Kingdom. Richard M. Jacques is a Senior Lecturer in Medical Statistics at the University of Sheffield in the United Kingdom.

Summary

STATISTICS AT SQUARE TWO

An easy-to-follow exploration of intermediate statistical techniques used in medical research

In the newly revised third edition of Statistics at Square Two: Understanding Modern Statistical Applications in Medicine, a team of distinguished statisticians delivers an accessible and intuitive discussion of advanced statistical methods for readers and users of scientific medical literature. This will allow readers to engage critically with modern research as the authors explain the correct interpretation of results in the medical literature.

The book includes two brand new chapters covering meta-analysis and time-series analysis as well as new references to the many checklists that have appeared in recent years to enable better reporting of contemporary research. Most examples have been updated as well, and each chapter contains practice exercises and answers. Readers will also find sample code (in R) for many of the analyses, in addition to:
* A thorough introduction to models and data, including the different types of data, statistical models, and computer-intensive methods
* Comprehensive explorations of multiple linear regression, including the interpretation of computer output, diagnostic statistics such as influential points, and many uses of multiple regression
* Practical discussions of multiple logistic regression, survival analysis, Poisson regression and random effects models including their uses, examples in the medical literature, and strategies for interpreting computer output

Perfect for anyone hoping to better understand the statistics presented in contemporary medical research, Statistics at Square Two: Understanding Modern Statistical Applications in Medicine will also benefit postgraduate students studying statistics and medicine.

Product details

• Authors: Michael J Campbell, Michael J. Campbell, Michael J. (University of Sheffield Campbell, Michael J. Jacques Campbell, MJ Campbell, Richard M Jacques, Richard M. Jacques
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Paperback / Softback
• Released: 31.12.2021
• EAN: 9781119401360
• ISBN: 978-1-119-40136-0
• No. of pages: 208
• Subjects: Natural sciences, medicine, IT, technology > Medicine > General; Statistik, Medizin, Medical Science, Klinische u. experimentelle medizinische Forschung, Clinical & Experimental Medical Research, Medical Statistics & Epidemiology, Medizinische Statistik u. Epidemiologie