A Textbook for the Health Sciences
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Köp båda 2 för 824 krSTEPHEN J. WALTERS is Professor of Medical Statistics and Clinical Trials in the School of Health and Related Research (ScHARR) at the University of Sheffield, UK. Stephen is a prolific researcher and writer, including the popular textbooks How to Display Data and How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research. He is a National Institute for Health Research (NIHR) Senior Investigator, and has developed several courses on teaching medical statistics to medical and health science students, clinicians and allied health professionals. MICHAEL J. CAMPBELL is Emeritus Professor of Medical Statistics in the School of Health and Related Research (ScHARR) at the University of Sheffield, UK. Mike is a leading researcher in medical statistics and clinical trials with a national and international reputation. A prolific writer, Mike has written many best-selling textbooks on medical statistics and clinical trials including: Statistics at Square One, Statistics at Square Two, Sample Size Tables for Clinical Studies, and How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research. DAVID MACHIN is Emeritus Professor of Medical Statistics in the School of Health and Related Research (ScHARR) at the University of Sheffield, UK. He was Foundation Director of the National Medical Research Council, Clinical Trials and Epidemiology Research Unit, Singapore, and Head of the MRC Cancer Trials Office, Cambridge, UK. He has published more than 250 peer reviewed articles, and several books on a wide variety of topics in statistics and medicine. His earlier experience included posts at the Universities of Wales, Leeds, Stirling, Southampton and Sheffield, a period with the European Organisation for Research and Treatment of Cancer in Brussels, Belgium, and at the World Health Organization in Geneva, Switzerland.
Preface xi 1 Uses and Abuses of Medical Statistics 1 1.1 Introduction 2 1.2 Why Use Statistics? 2 1.3 Statistics is About Common Sense and Good Design 3 1.4 How a Statistician Can Help 5 2 Displaying and Summarising Data 9 2.1 Types of Data 10 2.2 Summarising Categorical Data 13 2.3 Displaying Categorical Data 15 2.4 Summarising Continuous Data 17 2.5 Displaying Continuous Data 24 2.6 Within-Subject Variability 28 2.7 Presentation 30 2.8 Points When Reading the Literature 31 2.9 Technical Details 32 2.10 Exercises 33 3 Summary Measures for Binary Data 37 3.1 Summarising Binary and Categorical Data 38 3.2 Points When Reading the Literature 46 3.3 Exercises 46 4 Probability and Distributions 49 4.1 Types of Probability 50 4.2 The Binomial Distribution 54 4.3 The Poisson Distribution 55 4.4 Probability for Continuous Outcomes 57 4.5 The Normal Distribution 58 4.6 Reference Ranges 63 4.7 Other Distributions 64 4.8 Points When Reading the Literature 66 4.9 Technical Section 66 4.10 Exercises 67 5 Populations, Samples, Standard Errors and Confidence Intervals 71 5.1 Populations 72 5.2 Samples 73 5.3 The Standard Error 74 5.4 The Central Limit Theorem 75 5.5 Standard Errors for Proportions and Rates 77 5.6 Standard Error of Differences 79 5.7 Confidence Intervals for an Estimate 80 5.8 Confidence Intervals for Differences 83 5.9 Points When Reading the Literature 84 5.10 Technical Details 85 5.11 Exercises 86 6 Hypothesis Testing, P-values and Statistical Inference 91 6.1 Introduction 92 6.2 The Null Hypothesis 92 6.3 The Main Steps in Hypothesis Testing 94 6.4 Using Your P-value to Make a Decision About Whether to Reject, or Not Reject, Your Null Hypothesis 96 6.5 Statistical Power 99 6.6 One-sided and Two-sided Tests 101 6.7 Confidence Intervals (CIs) 101 6.8 Large Sample Tests for Two Independent Means or Proportions 104 6.9 Issues with P-values 107 6.10 Points When Reading the Literature 108 6.11 Exercises 108 7 Comparing Two or More Groups with Continuous Data 111 7.1 Introduction 112 7.2 Comparison of Two Groups of Paired Observations Continuous Outcomes 113 7.3 Comparison of Two Independent Groups Continuous Outcomes 119 7.4 Comparing More than Two Groups 127 7.5 Non-Normal Distributions 130 7.6 Degrees of Freedom 131 7.7 Points When Reading the Literature 132 7.8 Technical Details 132 7.9 Exercises 140 8 Comparing Groups of Binary and Categorical Data 145 8.1 Introduction 146 8.2 Comparison of Two Independent Groups Binary Outcomes 146 8.3 Comparing Risks 151 8.4 Comparison of Two Groups of Paired Observations Categorical Outcomes 152 8.5 Degrees of Freedom 153 8.6 Points When Reading the Literature 154 8.7 Technical Details 154 8.8 Exercises 160 9 Correlation and Linear Regression 163 9.1 Introduction 164 9.2 Correlation 165 9.3 Linear Regression 171 9.4 Comparison of Assumptions Between Correlation and Regression 178 9.5 Multiple Regression 179 9.6 Correlation is not Causation 181 9.7 Points When Reading the Literature 182 9.8 Technical Details 182 9.9 Exercises 190 10 Logistic Regression 193 10.1 Introduction 194 10.2 Binary Outcome Variable 194 10.3 The Multiple Logistic Regression Equation 196 10.4 Conditional Logistic Regression 200 10.5 Reporting the Results of a Logistic Regression 201 10.6 Additional Points When Reading the Literature When Logistic Regression Has Been Used 202 10.7 Technical Details 202 10.8 The Wald Test 204 10.9 Evaluating the Model and its Fit: The HosmerLemeshow Test 204 10.10 Assessing Predictive Efficiency (1): 2 2 Classification Table 205 10.11 Assessing Predictive Efficiency (2): The ROC Curve 206 10.12 Investigating Linearity 206 10.13 Exercises 207 11 Survival Analysis 211 11.1 Time to Event Data 212 11.2 KaplanMeier Survival Curve 214 11.3 The Logrank Test 217 11