Modern Medical Statistics: A Practical Guide

Prologue.

1. The Generalized Linear Model.

1.1 Introduction.

1.2 The generalized linear model – a brief non-technical account.

1.3 Examples of the application of generalized linear models.

1.4 Poisson regression.

1.5 Overdispersion.

1.6 Summary.

2. Generalized Linear Models for Longitudinal Data.

2.1 Introduction.

2.2 Marginal and conditional regression models.

2.3 Marginal and conditional regression models for continuous responses with Gaussian errors.

2.4 Marginal and conditional regression models for non-normal responses.

2.5 Summary.

3. Missing Values, Drop-outs, Compliance and Intention-to-Treat.

3.1 Introduction.

3.2 Missing values and drop-outs.

3.3 Modelling longitudinal data containing ignorable missing values.

3.4 Non-ignorable missing values.

3.5 Compliance and intention-to-treat.

3.6 Summary.

4. Generalized Additive Models.

4.1 Introduction.

4.2 Scatterplot smoothers.

4.3 Additive and generalized additive models.

4.4 Examples of the application of GAMs.

4.5 Summary.

5. Classification and Regression Trees.

5.1 Introduction.

5.2 Tree-based models.

5.3 Birthweight of babies.

5.4 Summary.

6. Survival Analysis I: Cox's Regression.

6.1 Introduction.

6.2 The survivor function.

6.3 The hazard function.

6.4 Cox's proportional hazards model.

6.5 Left truncation.

6.6 Extending Cox's model by stratification.

6.7 Checking the specification of a Cox model.

6.8 Summary.

7. Survival Analysis II: Time-dependent Covariates, Frailty and Tree Models.

7.1 Introduction.

7.2 Time-dependent covariates.

7.3 Random effects models for survival data.

7.4 Tree-structured survival analysis.

7.5 Summary.

8. Bayesian Methods and Meta-analysis.

8.1 Introduction.

8.2 Bayesian methods.

8.3 Meta-analysis.

8.4 Summary.

9. Exact Inference for Categorical Data.

9.1 Introduction.

9.2 Small expected values in contingency table, Yates' correction and Fisher's exact test.

9.3 Examples of the use of exact p-values.

9.4 Logistic regression and conditional logistic regression for sparse data.

9.5 Summary.

10. Finite Mixture Models.

10.1 Introduction.

10.2 Finite mixture distributions.

10.3 Estimating the parameters in finite mixture models.

10.4 Some examples of the application of finite mixture densities in medical research.

10.5 Latent class analysis – mixtures for binary data.

10.6 Summary.

Glossary.

Appendix A: Statistical Graphics in Medical Invetigations.

A.1 Introduction.

A.2 Probability plots.

A.3 Scatterplots and beyond.

A.4 Scatterplot matrices.

A.5 Coplots and trellis graphics.

Appendix B: Answers to Selected Exercises.

References.

Index.