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.
Author(s): Michael J. Campbell, Richard M. Jacques
Edition: 3
Publisher: Wiley-Blackwell
Year: 2023
Language: English
Commentary: PDF Quality: 99.99 % || [[trace-bullet]]
Pages: 208
City: Hoboken, NJ
Tags: Medical Statistics; Advanced Statistical Methods; Meta-Analysis; Time-Series Analysis; R Programming Language; Multiple Linear Regression; Diagnostic Statistics; Survival Analysis
Statistics at Square Two
Contents
Preface
1 Models, Tests and Data
1.1 Types of Data
1.2 Confounding, Mediation and Effect Modification
1.3 Causal Inference
1.4 Statistical Models
1.5 Results of Fitting Models
1.6 Significance Tests
1.7 Confidence Intervals
1.8 Statistical Tests Using Models
1.9 Many Variables
1.10 Model Fitting and Analysis: Exploratory and Confirmatory Analyses
1.11 Computer-intensive Methods
1.12 Missing Values
1.13 Bayesian Methods
1.14 Causal Modelling
1.15 Reporting Statistical Results in the Medical Literature
1.16 Reading Statistics in the Medical Literature
2 Multiple Linear Regression
2.1 The Model
2.2 Uses of Multiple Regression
2.3 Two Independent Variables
2.3.1 One Continuous and One Binary Independent Variable
2.3.2 Two Continuous Independent Variables
2.3.3 Categorical Independent Variables
2.4 Interpreting a Computer Output
2.4.1 One Continuous Variable
2.4.2 One Continuous Variable and One Binary Independent Variable
2.4.3 One Continuous Variable and One Binary Independent Variable with Their Interaction
2.4.4 Two Independent Variables: Both Continuous
2.4.5 Categorical Independent Variables
2.5 Examples in the Medical Literature
2.5.1 Analysis of Covariance: One Binary and One Continuous Independent Variable
2.5.2 Two Continuous Independent Variables
2.6 Assumptions Underlying the Models
2.7 Model Sensitivity
2.7.1 Residuals, Leverage and Influence
2.7.2 Computer Analysis: Model Checking and Sensitivity
2.8 Stepwise Regression
2.9 Reporting the Results of a Multiple Regression
2.10 Reading about the Results of a Multiple Regression
2.11 Frequently Asked Questions
2.12 Exercises: Reading the Literature
3 Multiple Logistic Regression
3.1 Quick Revision
3.2 The Model
3.2.1 Categorical Covariates
3.3 Model Checking
3.3.1 Lack of Fit
3.3.2 “Extra-binomial” Variation or “Over Dispersion”
3.3.3 The Logistic Transform is Inappropriate
3.4 Uses of Logistic Regression
3.5 Interpreting a Computer Output
3.5.1 One Binary Independent Variable
3.5.2 Two Binary Independent Variables
3.5.3 Two Continuous Independent Variables
3.6 Examples in the Medical Literature
3.6.1 Comment
3.7 Case-control Studies
3.8 Interpreting Computer Output: Unmatched Case-control Study
3.9 Matched Case-control Studies
3.10 Interpreting Computer Output: Matched Case-control Study
3.11 Example of Conditional Logistic Regression in the Medical Literature
3.11.1 Comment
3.12 Alternatives to Logistic Regression
3.13 Reporting the Results of Logistic Regression
3.14 Reading about the Results of Logistic Regression
3.15 Frequently Asked Questions
3.16 Exercise
4 Survival Analysis
4.1 Introduction
4.2 The Model
4.3 Uses of Cox Regression
4.4 Interpreting a Computer Output
4.5 Interpretation of the Model
4.6 Generalisations of the Model
4.6.1 Stratified Models
4.6.2 Time Dependent Covariates
4.6.3 Parametric Survival Models
4.6.4 Competing Risks
4.7 Model Checking
4.8 Reporting the Results of a Survival Analysis
4.9 Reading about the Results of a Survival Analysis
4.10 Example in the Medical Literature
4.10.1 Comment
4.11 Frequently Asked Questions
4.12 Exercises
5 Random Effects Models
5.1 Introduction
5.2 Models for Random Effects
5.3 Random vs Fixed Effects
5.4 Use of Random Effects Models
5.4.1 Cluster Randomised Trials
5.4.2 Repeated Measures
5.4.3 Sample Surveys
5.4.4 Multi-centre Trials
5.5 Ordinary Least Squares at the Group Level
5.6 Interpreting a Computer Output
5.6.1 Different Methods of Analysis
5.6.2 Likelihood and gee
5.6.3 Interpreting Computer Output
5.7 Model Checking
5.8 Reporting the Results of Random Effects Analysis
5.9 Reading about the Results of Random Effects Analysis
5.10 Examples of Random Effects Models in the Medical Literature
5.10.1 Cluster Trials
5.10.2 Repeated Measures
5.10.3 Comment
5.10.4 Clustering in a Cohort Study
5.10.5 Comment
5.11 Frequently Asked Questions
5.12 Exercises
6 Poisson and Ordinal Regression
6.1 Poisson Regression
6.2 The Poisson Model
6.3 Interpreting a Computer Output: Poisson Regression
6.4 Model Checking for Poisson Regression
6.5 Extensions to Poisson Regression
6.6 Poisson Regression Used to Estimate Relative Risks from a 2 × 2 Table
6.7 Poisson Regression in the Medical Literature
6.8 Ordinal Regression
6.9 Interpreting a Computer Output: Ordinal Regression
6.10 Model Checking for Ordinal Regression
6.11 Ordinal Regression in the Medical Literature
6.12 Reporting the Results of Poisson or Ordinal Regression
6.13 Reading about the Results of Poisson or Ordinal Regression
6.14 Frequently Asked Question
6.15 Exercises
7 Meta-analysis
7.1 Introduction
7.2 Models for Meta-analysis
7.3 Missing Values
7.4 Displaying the Results of a Meta-analysis
7.5 Interpreting a Computer Output
7.6 Examples from the Medical Literature
7.6.1 Example of a Meta-analysis of Clinical Trials
7.6.2 Example of a Meta-analysis of Case-control Studies
7.7 Reporting the Results of a Meta-analysis
7.8 Reading about the Results of a Meta-analysis
7.9 Frequently Asked Questions
7.10 Exercise
8 Time Series Regression
8.1 Introduction
8.2 The Model
8.3 Estimation Using Correlated Residuals
8.4 Interpreting a Computer Output: Time Series Regression
8.5 Example of Time Series Regression in the Medical Literature
8.6 Reporting the Results of Time Series Regression
8.7 Reading about the Results of Time Series Regression
8.8 Frequently Asked Questions
8.9 Exercise
Appendix 1 Exponentials and Logarithms
Appendix 2 Maximum Likelihood and Significance Tests
A2.1 Binomial Models and Likelihood
A2.2 The Poisson Model
A2.3 The Normal Model
A2.4 Hypothesis Testing: the Likelihood Ratio Test
A2.5 The Wald Test
A2.6 The Score Test
A2.7 Which Method to Choose?
A2.8 Confidence Intervals
A2.9 Deviance Residuals for Binary Data
A2.10 Example: Derivation of the Deviances and Deviance Residuals Given in Table 3.3
A2.10.1 Grouped Data
A2.10.2 Ungrouped Data
Appendix 3 Bootstrapping and Variance Robust Standard Errors
A3.1 The Bootstrap
A3.2 Example of the Bootstrap
A3.3 Interpreting a Computer Output: The Bootstrap
A3.3.1 Two-sample T-test with Unequal Variances
A3.4 The Bootstrap in the Medical Literature
A3.5 Robust or Sandwich Estimate SEs
A3.6 Interpreting a Computer Output: Robust SEs for Unequal Variances
A3.7 Other Uses of Robust Regression
A3.8 Reporting the Bootstrap and Robust SEs in the Literature
A3.9 Frequently Asked Question
Appendix 4 Bayesian Methods
A4.1 Bayes’ Theorem
A4.2 Uses of Bayesian Methods
A4.3 Computing in Bayes
A4.4 Reading and Reporting Bayesian Methods in the Literature
A4.5 Reading about the Results of Bayesian Methods in the Medical Literature
Appendix 5 R codes
A5.1 R Code for Chapter 2
A5.3 R Code for Chapter 3
A5.4 R Code for Chapter 4
A5.5 R Code for Chapter 5
A5.6 R Code for Chapter 6
A5.7 R Code for Chapter 7
A5.8 R Code for Chapter 8
A5.9 R Code for Appendix 1
A5.10 R Code for Appendix 2
A5.11 R Code for Appendix 3
Answers to Exercises
Glossary
Index
EULA
