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Informationen zum Autor BRIAN S EVERITT is Professor of Behavioural Statistics and Head of the Biostatistics and Computing Department at the Institute of Psychiatry, King's College, London. Klappentext Statistical science plays an increasingly important role in medical research. Over the last few decades, many new statistical methods have been developed which have particular relevance for medical researchers and, with the appropriate software now easily available, these techniques can be used almost routinely to great effect. These innovative methods include survival analysis, generalized additive models and Bayesian methods. MODERN MEDICAL STATISTICS covers these essential new techniques at an accessible technical level, its main focus being not on the theory but on the effective practical application of these methods in medical research. Exercises are provided at the end of each chapter Numerous practical examples throughout Up-to-date information on the software packages available that relate directly to the techniques covered in each chapter Glossary MODERN MEDICAL STATISTICS is an indispensable practical guide for medical researchers and medical statisticians as well as an ideal text for advanced courses in medical statistics and public health. Zusammenfassung Statistical science plays an increasingly important role in medical research. Over the last few decades! many new statistical methods have been developed which have particular relevance for medical researchers and! with the appropriate software now easily available! these techniques can be used almost routinely to great effect. Inhaltsverzeichnis Preface. 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, Yat...
