Statistics at Square Two: Understanding modern statistical applications in medicine -- 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: ungrouped analysis -- 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 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 Generalisations of the model -- 4.8 Model checking -- 4.9 Reporting the results of a survival analysis.

4.10 Reading about the results of a survival analysis -- Chapter 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 -- 5.5 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 SE -- 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 exercises -- Glossary -- Index.