Using and Understanding Medical Statistics
Autor Char Leungen Limba Engleză Paperback – 12 noi 2026
Many clinicians lack the statistical training needed to critically appraise published research, yet evidence-based practice demands exactly that skill. Using and Understanding Medical Statistics takes a deliberately visual, clinician-centred approach, using over 200 figures and real-world journal article examples to build intuitive understanding of the methods encountered in medical literature.
Coverage spans descriptive statistics, inferential statistics and confidence intervals, linear and logistic regression, survival analysis, and advanced observational techniques including propensity score matching and interrupted time series. Each chapter closes with simulation-based exercises that mirror genuine medical research scenarios, reinforcing practical application of each technique.
The book also features:
- "Key Concepts" summaries at the end of each section that scaffold understanding and prepare readers for subsequent topics
- Technical sections marked by a chili icon at subheadings, allowing readers to skip advanced material without losing continuity
- Chapter summaries providing high-level overviews that support rapid review and consolidation of statistical methods covered
- Examples inspired by published journal articles rather than generic datasets, grounding every technique in authentic clinical contexts
- Mathematical notation kept to an absolute minimum, connecting high-school mathematics to the statistical sophistication of modern research
Preț: 410.20 lei
Preț vechi: 431.79 lei
-5% Precomandă
Puncte Express: 615
Carte nepublicată încă
Livrare prin curier în România Precomanda se expediază când titlul devine disponibil.
Transport gratuit pentru acest produs Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
Doresc să fiu notificat când acest titlu va fi disponibil:
Se trimite...
Specificații
Notă biografică
Char Leung is a Lecturer in Epidemiology and Medical Statistics at the University of Leicester, where he teaches students enrolled in the MBChB and B. Clinical Sciences programs, and supervises undergraduate and postgraduate students at the University of Leicester and the University of Cambridge.
Cuprins
Preface xiii
Part I Introduction 1
1 Introduction 3
1.1 Purpose of and Approach Used in This Book 3
1.2 Why Statistics in Medical Research? 6
1.3 Fundamental Reasoning in Medical Research: Causation and Association 9
1.4 Road Map of the Book 13
1.5 Summary 14
1.6 Exercise 14
2 Brief Overview of Medical Research 16
2.1 Biases and Confounding 16
2.2 Types of Study Designs 23
2.3 Reading a Medical Research Paper 40
2.4 Summary 42
2.5 Exercise 42
Part II Fundamentals of Statistics 45
3 Level of Measurement 47
3.1 Numerical Variables 48
3.2 Ordinal Variables 50
3.3 Categorical Variables 51
3.4 Data Conversion 51
3.5 Summary 54
3.6 Exercise 54
4 Descriptive Statistics 56
4.1 Central Tendency 56
4.2 Dispersion 60
4.3 Correlation 64
4.4 Other Measure of Association 71
4.5 Summary 76
4.6 Exercise 76
5 Inferential Statistics 79
5.1 Population, Sample and Hypothetical Repeated Sampling 79
5.2 Confidence Intervals 82
5.3 Hypothesis Testing 85
5.4 Summary 98
5.5 Exercise 98
Part III Regression Analysis 103
6 Linear Regression 107
6.1 Underlying Principles 107
6.2 The Regression Model and Interpretation 109
6.3 Confidence Intervals and Hypothesis Tests 114
6.4 Goodness-of-fit 118
6.5 Partial R2 120
6.6 Categorical Data and Multicollinearity 120
6.7 Interaction Effect 124
6.8 Joint Tests 125
6.9 Assumptions of Linear Regression 127
6.10 Drawback of Linear Regression 128
6.11 Summary 128
6.12 Exercise 129
7 Logistic Regression 133
7.1 Underlying Principles 133
7.2 The Regression Model and Interpretation 138
7.3 Confidence Intervals and Hypothesis Tests 139
7.4 Goodness-of-fit 141
7.5 Assumptions of Logistic Regression 145
7.6 Drawback of Logistic Regression 145
7.7 Summary 146
7.8 Exercises 146
8 Cox Regression 150
8.1 Underlying Principles 152
8.2 The Regression Model and Interpretation 164
8.3 Confidence Intervals and Hypothesis Tests 166
8.4 Goodness-of-fit 170
8.5 Assumptions of Cox Regression 172
8.6 Drawback of Cox Regression 174
8.7 Summary 174
8.8 Exercises 175
Part IV Advanced Topics 181
9 Multiple Comparison 183
9.1 Testing the Difference Between Multiple Groups 183
9.2 Post Hoc and A-priori Tests 187
9.3 Summary 193
9.4 Exercise 193x Contents
10 Propensity Score Matching 196
10.1 Propensity Scores 196
10.2 Matching Methods 199
10.3 Mahalanobis Distance 202
10.4 Assumptions 204
10.5 Summary 205
10.6 Exercise 205
11 Interrupted Time Series Analysis 208
11.1 Time-dependent Structure 210
11.2 Segmented Regression 211
11.3 Autoregressive Integrated Moving Average (ARIMA) Models 213
11.4 Summary 222
11.5 Exercise 222
12 Meta-analysis 227
12.1 Repeated Sampling Principle 229
12.2 Fixed-effect Model 230
12.3 Random-effect Model 235
12.4 Assessing Heterogeneity 241
12.5 Assessing and Adjusting for Publication Bias 244
12.6 Summary 247
12.7 Exercise 247
13 Beyond the Basics: Extensions and Advanced Approaches 253
13.1 Other Measures of Correlation and Association 253
13.2 Non-linear Regression 258
13.3 Multinomial Logistic Regression 259
13.4 Other Models in Survival Analysis: Non-proportional Hazard and Competing Risk Model 261
13.5 Multiple Multivariate Comparison 272
13.6 Multivariate Interrupted Time Series Analysis 274Contents xi
13.7 Further Topics in Meta-analysis: Addressing Heterogeneity and Network Meta-analysis (NMA) 277
13.8 Regression Models for Count Data 294
13.9 PCA and FA 299
13.10 E-value: Addressing the Impact of Unmeasured Confounding 306
13.11 Summary 313
13.12 Exercise 314
Glossary 336
Index 349
Part I Introduction 1
1 Introduction 3
1.1 Purpose of and Approach Used in This Book 3
1.2 Why Statistics in Medical Research? 6
1.3 Fundamental Reasoning in Medical Research: Causation and Association 9
1.4 Road Map of the Book 13
1.5 Summary 14
1.6 Exercise 14
2 Brief Overview of Medical Research 16
2.1 Biases and Confounding 16
2.2 Types of Study Designs 23
2.3 Reading a Medical Research Paper 40
2.4 Summary 42
2.5 Exercise 42
Part II Fundamentals of Statistics 45
3 Level of Measurement 47
3.1 Numerical Variables 48
3.2 Ordinal Variables 50
3.3 Categorical Variables 51
3.4 Data Conversion 51
3.5 Summary 54
3.6 Exercise 54
4 Descriptive Statistics 56
4.1 Central Tendency 56
4.2 Dispersion 60
4.3 Correlation 64
4.4 Other Measure of Association 71
4.5 Summary 76
4.6 Exercise 76
5 Inferential Statistics 79
5.1 Population, Sample and Hypothetical Repeated Sampling 79
5.2 Confidence Intervals 82
5.3 Hypothesis Testing 85
5.4 Summary 98
5.5 Exercise 98
Part III Regression Analysis 103
6 Linear Regression 107
6.1 Underlying Principles 107
6.2 The Regression Model and Interpretation 109
6.3 Confidence Intervals and Hypothesis Tests 114
6.4 Goodness-of-fit 118
6.5 Partial R2 120
6.6 Categorical Data and Multicollinearity 120
6.7 Interaction Effect 124
6.8 Joint Tests 125
6.9 Assumptions of Linear Regression 127
6.10 Drawback of Linear Regression 128
6.11 Summary 128
6.12 Exercise 129
7 Logistic Regression 133
7.1 Underlying Principles 133
7.2 The Regression Model and Interpretation 138
7.3 Confidence Intervals and Hypothesis Tests 139
7.4 Goodness-of-fit 141
7.5 Assumptions of Logistic Regression 145
7.6 Drawback of Logistic Regression 145
7.7 Summary 146
7.8 Exercises 146
8 Cox Regression 150
8.1 Underlying Principles 152
8.2 The Regression Model and Interpretation 164
8.3 Confidence Intervals and Hypothesis Tests 166
8.4 Goodness-of-fit 170
8.5 Assumptions of Cox Regression 172
8.6 Drawback of Cox Regression 174
8.7 Summary 174
8.8 Exercises 175
Part IV Advanced Topics 181
9 Multiple Comparison 183
9.1 Testing the Difference Between Multiple Groups 183
9.2 Post Hoc and A-priori Tests 187
9.3 Summary 193
9.4 Exercise 193x Contents
10 Propensity Score Matching 196
10.1 Propensity Scores 196
10.2 Matching Methods 199
10.3 Mahalanobis Distance 202
10.4 Assumptions 204
10.5 Summary 205
10.6 Exercise 205
11 Interrupted Time Series Analysis 208
11.1 Time-dependent Structure 210
11.2 Segmented Regression 211
11.3 Autoregressive Integrated Moving Average (ARIMA) Models 213
11.4 Summary 222
11.5 Exercise 222
12 Meta-analysis 227
12.1 Repeated Sampling Principle 229
12.2 Fixed-effect Model 230
12.3 Random-effect Model 235
12.4 Assessing Heterogeneity 241
12.5 Assessing and Adjusting for Publication Bias 244
12.6 Summary 247
12.7 Exercise 247
13 Beyond the Basics: Extensions and Advanced Approaches 253
13.1 Other Measures of Correlation and Association 253
13.2 Non-linear Regression 258
13.3 Multinomial Logistic Regression 259
13.4 Other Models in Survival Analysis: Non-proportional Hazard and Competing Risk Model 261
13.5 Multiple Multivariate Comparison 272
13.6 Multivariate Interrupted Time Series Analysis 274Contents xi
13.7 Further Topics in Meta-analysis: Addressing Heterogeneity and Network Meta-analysis (NMA) 277
13.8 Regression Models for Count Data 294
13.9 PCA and FA 299
13.10 E-value: Addressing the Impact of Unmeasured Confounding 306
13.11 Summary 313
13.12 Exercise 314
Glossary 336
Index 349