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Statistics at Square Two 3rd edition [Pehme köide]

(University of Sheffield, UK), (University of Sheffield, UK)
  • Formaat: Paperback / softback, 208 pages, kõrgus x laius x paksus: 216x138x13 mm, kaal: 369 g
  • Ilmumisaeg: 09-Mar-2023
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 1119401364
  • ISBN-13: 9781119401360
Teised raamatud teemal:
Statistics at Square Two 3rd editionSuurem pilt
  • Formaat: Paperback / softback, 208 pages, kõrgus x laius x paksus: 216x138x13 mm, kaal: 369 g
  • Ilmumisaeg: 09-Mar-2023
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 1119401364
  • ISBN-13: 9781119401360
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119401360.html
Märksõnad:
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.
Preface xi
1 Models, Tests and Data
1(16)
1.1 Types of Data
1(1)
1.2 Confounding, Mediation and Effect Modification
2(1)
1.3 Causal Inference
3(2)
1.4 Statistical Models
5(1)
1.5 Results of Fitting Models
6(1)
1.6 Significance Tests
7(1)
1.7 Confidence Intervals
8(1)
1.8 Statistical Tests Using Models
8(1)
1.9 Many Variables
9(1)
1.10 Model Fitting and Analysis: Exploratory and Confirmatory Analyses
10(1)
1.11 Computer-intensive Methods
11(1)
1.12 Missing Values
11(11)
1.13 Bayesian Methods
22(1)
1.14 Causal Modelling
22(2)
1.15 Reporting Statistical Results in the Medical Literature
24
1.16 Reading Statistics in the Medical Literature
14(3)
2 Multiple Linear Regression
17(24)
2.1 The Model
17(1)
2.2 Uses of Multiple Regression
18(1)
2.3 Two Independent Variables
18(5)
2.3.1 One Continuous and One Binary Independent Variable
19(3)
2.3.2 Two Continuous Independent Variables
22(1)
2.3.3 Categorical Independent Variables
22(1)
2.4 Interpreting a Computer Output
23(8)
2.4.1 One Continuous Variable
24(1)
2.4.2 One Continuous Variable and One Binary Independent Variable
25(1)
2.4.3 One Continuous Variable and One Binary Independent Variable with Their Interaction
26(1)
2.4.4 Two Independent Variables: Both Continuous
27(2)
2.4.5 Categorical Independent Variables
29(2)
2.5 Examples in the Medical Literature
31(1)
2.5.1 Analysis of Covariance: One Binary and One Continuous Independent Variable
31(1)
2.5.2 Two Continuous Independent Variables
32(1)
2.6 Assumptions Underlying the Models
32(1)
2.7 Model Sensitivity
33(2)
2.7.1 Residuals, Leverage and Influence
33(1)
2.7.2 Computer Analysis: Model Checking and Sensitivity
34(1)
2.8 Stepwise Regression
35(1)
2.9 Reporting the Results of a Multiple Regression
36(1)
2.10 Reading about the Results of a Multiple Regression
36(1)
2.11 Frequently Asked Questions
37(1)
2.12 Exercises: Reading the Literature
38(3)
3 Multiple Logistic Regression
41(24)
3.1 Quick Revision
41(1)
3.2 The Model
42(2)
3.2.1 Categorical Covariates
44(1)
3.3 Model Checking
44(2)
3.3.1 Lack of Fit
45(1)
3.3.2 "Extra-binomial" Variation or "Over Dispersion"
45(1)
3.3.3 The Logistic Transform is Inappropriate
46(1)
3.4 Uses of Logistic Regression
46(1)
3.5 Interpreting a Computer Output
47(7)
3.5.1 One Binary Independent Variable
47(4)
3.5.2 Two Binary Independent Variables
51(2)
3.5.3 Two Continuous Independent Variables
53(1)
3.6 Examples in the Medical Literature
54(2)
3.6.1 Comment
55(1)
3.7 Case-control Studies
56(1)
3.8 Interpreting Computer Output: Unmatched Case-control Study
56(2)
3.9 Matched Case-control Studies
58(1)
3.10 Interpreting Computer Output: Matched Case-control Study
58(2)
3.11 Example of Conditional Logistic Regression in the Medical Literature
60(1)
3.11.1 Comment
60(1)
3.12 Alternatives to Logistic Regression
61(1)
3.13 Reporting the Results of Logistic Regression
61(1)
3.14 Reading about the Results of Logistic Regression
61(1)
3.15 Frequently Asked Questions
62(1)
3.16 Exercise
62(3)
4 Survival Analysis
65(14)
4.1 Introduction
65(1)
4.2 The Model
66(2)
4.3 Uses of Cox Regression
68(1)
4.4 Interpreting a Computer Output
68(2)
4.5 Interpretation of the Model
70(1)
4.6 Generalisations of the Model
70(2)
4.6.1 Stratified Models
70(1)
4.6.2 Time Dependent Covariates
71(1)
4.6.3 Parametric Survival Models
71(1)
4.6.4 Competing Risks
71(1)
4.7 Model Checking
72(1)
4.8 Reporting the Results of a Survival Analysis
73(1)
4.9 Reading about the Results of a Survival Analysis
74(1)
4.10 Example in the Medical Literature
74(2)
4.10.1 Comment
75(1)
4.11 Frequently Asked Questions
76(1)
4.12 Exercises
77(2)
5 Random Effects Models
79(16)
5.1 Introduction
79(1)
5.2 Models for Random Effects
80(1)
5.3 Random vs Fixed Effects
81(1)
5.4 Use of Random Effects Models
81(3)
5.4.1 Cluster Randomised Trials
81(1)
5.4.2 Repeated Measures
82(1)
5.4.3 Sample Surveys
83(1)
5.4.4 Multi-centre Trials
83(1)
5.5 Ordinary Least Squares at the Group Level
84(1)
5.6 Interpreting a Computer Output
85(4)
5.6.1 Different Methods of Analysis
85(1)
5.6.2 Likelihood and gee
85(1)
5.6.3 Interpreting Computer Output
86(3)
5.7 Model Checking
89(1)
5.8 Reporting the Results of Random Effects Analysis
89(1)
5.9 Reading about the Results of Random Effects Analysis
90(1)
5.10 Examples of Random Effects Models in the Medical Literature
90(1)
5.10.1 Cluster Trials
90(1)
5.10.2 Repeated Measures
91(1)
5.10.3 Comment
91(1)
5.10.4 Clustering in a Cohort Study
91(1)
5.10.5 Comment
91(1)
5.11 Frequently Asked Questions
91(1)
5.12 Exercises
92(3)
6 Poisson and Ordinal Regression
95(12)
6.1 Poisson Regression
95(1)
6.2 The Poisson Model
95(1)
6.3 Interpreting a Computer Output: Poisson Regression
96(1)
6.4 Model Checking for Poisson Regression
97(2)
6.5 Extensions to Poisson Regression
99(1)
6.6 Poisson Regression Used to Estimate Relative Risks from a 2 × 2 Table
99(1)
6.7 Poisson Regression in the Medical Literature
100(1)
6.8 Ordinal Regression
100(1)
6.9 Interpreting a Computer Output: Ordinal Regression
101(2)
6.10 Model Checking for Ordinal Regression
103(1)
6.11 Ordinal Regression in the Medical Literature
104(1)
6.12 Reporting the Results of Poisson or Ordinal Regression
104(1)
6.13 Reading about the Results of Poisson or Ordinal Regression
104(1)
6.14 Frequently Asked Question
105(1)
6.15 Exercises
105(2)
7 Meta-analysis
107(14)
7.1 Introduction
107(1)
1.2 Models for Meta-analysis
108(3)
7.3 Missing Values
111(1)
7.4 Displaying the Results of a Meta-analysis
111(2)
7.5 Interpreting a Computer Output
113(1)
7.6 Examples from the Medical Literature
114(1)
7.6.1 Example of a Meta-analysis of Clinical Trials
114(1)
7.6.2 Example of a Meta-analysis of Case-control Studies
115(1)
1.1 Reporting the Results of a Meta-analysis
115(1)
7.8 Reading about the Results of a Meta-analysis
116(1)
7.9 Frequently Asked Questions
116(2)
7.10 Exercise
118(3)
8 Time Series Regression
121(8)
8.1 Introduction
121(1)
8.2 The Model
122(1)
8.3 Estimation Using Correlated Residuals
122(1)
8.4 Interpreting a Computer Output: Time Series Regression
123(1)
8.5 Example of Time Series Regression in the Medical Literature
124(1)
8.6 Reporting the Results of Time Series Regression
125(1)
8.7 Reading about the Results of Time Series Regression
125(1)
8.8 Frequently Asked Questions
125(1)
8.9 Exercise
126(3)
Appendix 1 Exponentials and Logarithms
129(4)
Appendix 2 Maximum Likelihood and Significance Tests
133(10)
A2.1 Binomial Models and Likelihood
133(2)
A2.2 The Poisson Model
135(1)
A2.3 The Normal Model
135(2)
A2.4 Hypothesis Testing: the Likelihood Ratio Test
137(1)
A2.5 The Wald Test
138(1)
A2.6 The Score Test
138(1)
A2.7 Which Method to Choose?
139(1)
A2.8 Confidence Intervals
139(1)
A2.9 Deviance Residuals for Binary Data
140(1)
A2.10 Example: Derivation of the Deviances and Deviance Residuals Given in Table 3.3
140(3)
A2.10.1 Grouped Data
140(1)
A2.10.2 Ungrouped Data
140(3)
Appendix 3 Bootstrapping and Variance Robust Standard Errors
143(8)
A3.1 The Bootstrap
143(1)
A3.2 Example of the Bootstrap
144(1)
A3.3 Interpreting a Computer Output: The Bootstrap
145(1)
A3.3.1 Two-sample T-test with Unequal Variances
145(1)
A3.4 The Bootstrap in the Medical Literature
145(1)
A3.5 Robust or Sandwich Estimate SEs
146(1)
A3.6 Interpreting a Computer Output: Robust SEs for Unequal Variances
147(2)
A3.7 Other Uses of Robust Regression
149(1)
A3.8 Reporting the Bootstrap and Robust SEs in the Literature
149(1)
A3.9 Frequently Asked Question
150(1)
Appendix 4 Bayesian Methods
151(6)
A4.1 Bayes' Theorem
151(1)
A4.2 Uses of Bayesian Methods
152(1)
A4.3 Computing in Bayes
153(1)
A4.4 Reading and Reporting Bayesian Methods in the Literature
154(1)
A4.5 Reading about the Results of Bayesian Methods in the Medical Literature
154(3)
Appendix 5 R codes
157(22)
A5.1 R Code for
Chapter 2
157(6)
A5.3 R Code for
Chapter 3
163(3)
A5.4 R Code for
Chapter 4
166(2)
A5.5 R Code for
Chapter 5
168(2)
A5.6 R Code for
Chapter 6
170(1)
A5.7 R Code for
Chapter 7
171(2)
A5.8 R Code for
Chapter 8
173(1)
A5.9 R Code for Appendix 1
173(1)
A5.10 R Code for Appendix 2
174(1)
A5.11 R Code for Appendix 3
175(4)
Answers to Exercises 179(6)
Glossary 185(6)
Index 191
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.