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Statistics at Square Two, 2nd Edition
Comments from the Stata technical groupStatistics at Square Two, Second Edition, by Michael J. Campbell, is an updated version of the sequel (published in 2001) to the popular Statistics at Square One, published more than 30 years ago. At just over 130 pages, this text contains a lot of useful information. Intended as a quick reference of statistical methods prevalent in medical literature, Statistics at Square Two does not contain much theory; instead, Campbell focuses on the practical application of these methods. The text explains the benefits and pitfalls of these various methodologies and includes complete references, exercises, and frequently asked questions at the end of each chapter. Campbell uses Stata 8 throughout this text. Like its predecessor, this text covers classical analyses such as means and medians, t tests, and chi-squared tests. The text also covers the more complex methods that have become available to medical researchers through advances in computer technology. The newer methods the text covers include multiple regression, logistic regression, survival analysis including Cox regression, random-effects models, and Poisson and ordinal regression. Table of contents Preface
Chapter 1: Models, tests and data
1.1 Basics
1.2 Models 1.3 Types of data 1.4 Significance tests 1.5 Confidence intervals 1.6 Statistical tests using models 1.7 Model fitting and analysis: confirmatory and exploratory analyses 1.8 Computer-intensive methods 1.9 Bayesian methods 1.10 Missing values 1.11 Reporting statistical results in the literature 1.12 Reading statistics in the literature Chapter 2: Multiple linear regression
2.1 The model
2.2 Uses of multiple regression 2.3 Two independent variables 2.4 Interpreting a computer output 2.5 Multiple regression in action 2.6 Assumptions underlying the models 2.7 Model sensitivity 2.8 Stepwise regression 2.9 Reporting the results of a multiple regression 2.10 Reading the results of a multiple regression Chapter 3: Logistic regression
3.1 The model
3.2 Uses of logistic regression 3.3 Interpreting a computer output: grouped analysis 3.4 Logistic regression in action 3.5 Model checking 3.6 Interpreting computer output: grouped analysis 3.7 Case–control studies 3.8 Interpreting computer output: unmatched case–control studies 3.9 Matched case–control studies 3.10 Interpreting computer output: matched case–control studies 3.11 Conditional logistic regression in action 3.12 Reporting the results of logistic regression 3.13 Reading about logistic regression Chapter 4: Survival analysis
4.1 Introduction
4.2 The model 4.3 Uses of Cox regression 4.4 Interpreting a computer output 4.5 Survival analysis in action 4.6 Interpretation of the model 4.7 Generalizations of the model 4.8 Model checking 4.9 Reporting the results of a survival model 4.10 Reading about the results of a survival model Chapter 5: Random effects model
5.1 Introduction
5.2 Models for random effects 5.3 Random vs. fixed effects 5.4 Use of random effects models 5.5 Random effects models in action 5.6 Ordinary least squares at the group level 5.7 Computer analysis 5.8 Model checking 5.9 Reporting the results of random effects analysis 5.10 Reading about the results of random effects analysis Chapter 6: Other models
6.1 Poisson regression
6.2 Ordinal regression 6.3 Time series regression 6.4 Reporting Poisson, ordinal or time series regression in the literature 6.5 Reading about the results of Poisson, ordinal or time series regression in the literature Appendix 1: Exponentials and logarithms
A1.1 Logarithms
Appendix 2: Maximum likelihood and significance tests
A2.1 Binomial models and likelihood
A2.2 Poisson model A2.3 Normal model A2.4 Hypothesis testing: LR test A2.5 Wald test A2.6 Score test A2.7 Which method to choose? A2.8 Confidence intervals Appendix 3: Bootstrapping and variance robust standard errors
A3.1 Computer analysis
A3.2 The bootstrap in action A3.3 Robust or sandwich estimate A3.4 Reporting the bootstrap and robust SEs in the literature Appendix 4: Bayesian methods
A4.1 Reporting Bayesian methods in the literature
Answers to exercise
Glossary
Index
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