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Understanding Biostatistics looks at the fundamentals of biostatistics, using elementary statistics to explore the nature of statistical tests.
This book is intended to complement first-year statistics and biostatistics textbooks. The main focus here is on ideas, rather than on methodological details. Basic concepts are illustrated with representations from history, followed by technical discussions on what different statistical methods really mean. Graphics are used extensively throughout the book in order to introduce mathematical formulae in an accessible way.
Key features:
Discusses confidence intervals and p-values in terms of confidence functions.
Explains basic statistical methodology represented in terms of graphics rather than mathematical formulae, whilst highlighting the mathematical basis of biostatistics.
Looks at problems of estimating parameters in statistical models and looks at the similarities between different models.
Provides an extensive discussion on the position of statistics within the medical scientific process.
Discusses distribution functions, including the Guassian distribution and its importance in biostatistics.
This book will be useful for biostatisticians with little mathematical background as well as those who want to understand the connections in biostatistics and mathematical issues.
Autorentext
Anders Källén, Department of Biostatistics, AstraZeneca R&D, Sweden.
Zusammenfassung
Understanding Biostatistics looks at the fundamentals of biostatistics, using elementary statistics to explore the nature of statistical tests.
This book is intended to complement first-year statistics and biostatistics textbooks. The main focus here is on ideas, rather than on methodological details. Basic concepts are illustrated with representations from history, followed by technical discussions on what different statistical methods really mean. Graphics are used extensively throughout the book in order to introduce mathematical formulae in an accessible way.
Key features:
Inhalt
Preface ix
1 Statistics and medical science 1
1.1 Introduction 1
1.2 On the nature of science 3
1.3 How the scientific method uses statistics 5
1.4 Finding an outcome variable to assess your hypothesis 7
1.5 How we draw medical conclusions from statistical results 8
1.6 A few words about probabilities 13
1.7 The need for honesty: the multiplicity issue 16
1.8 Prespecification and p-value history 19
1.9 Adaptive designs: controlling the risks in an experiment 21
1.10 The elusive concept of probability 23
1.11 Comments and further reading 26
References 27
2 Observational studies and the need for clinical trials 29
2.1 Introduction 29
2.2 Investigations of medical interventions and risk factors 29
2.3 Observational studies and confounders 33
2.4 The experimental study 39
2.5 Population risks and individual risks 42
2.6 Confounders, Simpson's paradox and stratification 44
2.7 On incidence and prevalence in epidemiology 51
2.8 Comments and further reading 53
References 54
3 Study design and the bias issue 57
3.1 Introduction 57
3.2 What bias is all about 58
3.3 The need for a representative sample: on selection bias 58
3.4 Group comparability and randomization 61
3.5 Information bias in a cohort study 65
3.6 The study, or placebo, effect 68
3.7 The curse of missing values 70
3.8 Approaches to data analysis: avoiding self-inflicted bias 75
3.9 On meta-analysis and publication bias 79
3.10 Comments and further reading 81
References 82
4 The anatomy of a statistical test 85
4.1 Introduction 85
4.2 Statistical tests, medical diagnosis and Roman law 85
4.3 The risks with medical diagnosis 87
4.3.1 Medical diagnosis based on a single test 87
4.3.2 Bayes' theorem and the use and misuse of screening tests 89
4.4 The law: a non-quantitative analogue 91
4.5 Risks in statistical testing 93
4.5.1 Does tonsillectomy increase the risk of Hodgkin's lymphoma? 93
4.5.2 General discussion about statistical tests 98
4.6 Making statements about a binomial parameter 101
4.6.1 The frequentist approach 101
4.6.2 The Bayesian approach 104
4.7 The bell-shaped error distribution 109
4.8 Comments and further reading 112
References 113
4.A Appendix: The evolution of the central limit theorem 115
5 Learning about parameters, and some notes on planning 119
5.1 Introduction 119
5.2 Test statistics described by parameters 120
5.3 How we describe our knowledge about a parameter from an experiment 122
5.4 Statistical analysis of two proportions 127
5.4.1 Some ways to compare two proportions 127
5.4.2 Analysis of the group difference 130
5.5 Adjusting for confounders in the analysis 133
5.6 The power curve of an experiment 138
5.7 Some confusing aspects of power calculations 143
5.8 Comments and further reading 145
References 145
5.A Appendix: Some technical comments 146
5.A.1 The non-central hypergeometric distribution and 2 × 2 tables 146
5.A.2 The gamma and 2 distributions 147
6 Empirical distribution functions 149
6.1 Introduction 149
6.2 How to describe the distribution of a sample 149
6.3 Describing the sample: descriptive statistics 153
6.4 Population distribution parameters 156
6.5 Confidence in the CDF and its parameters 158
6.6 Analysis of paired data 162
6.7 Bootstrapping 163
6.8 Meta-analysis and heterogeneity 166
6.9 Comments and further reading 169
References 170
6.A Appendix: Some technical comments 171 6.A.1 The ext...