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<p>Contents</p> <p>Preface xv</p> <p>About the Companion Website xxi</p> <p><b>1 Philosophy of Science and Introduction to Epidemiology 1</b></p> <p>Introduction and Learning Objectives 1</p> <p>1.1 Approaches to Scientific Research 2</p> <p>1.1.1 History and Nature of Scientific Research 2</p> <p>1.1.2 What is Epidemiology? 6</p> <p>1.1.3 What are Statistics? 7</p> <p>1.1.4 Approach to Learning 8</p> <p>1.2 Formulating a Research Question 8</p> <p>1.2.1 Importance of a Well-Defined Research Question 8</p> <p>1.2.2 Development of Research Ideas 10</p> <p>1.3 Rates: Incidence and Prevalence 11</p> <p>1.3.1 Why Do We Need Rates? 11</p> <p>1.3.2 Measures of Disease Frequency 12</p> <p>1.3.3 Prevalence Rate 12</p> <p>1.3.4 Incidence Rate 12</p> <p>1.3.5 Relationship Between Incidence, Duration, and Prevalence 15</p> <p>1.4 Concepts of Prevention 16</p> <p>1.4.1 Introduction 16</p> <p>1.4.2 Primary, Secondary, and Tertiary Prevention 17</p> <p>1.5 Answers to Self-Assessment Exercises 18</p> <p><b>2 Routine Data Sources and Descriptive Epidemiology 25</b></p> <p>Introduction and Learning Objectives 25</p> <p>2.1 Routine Collection of Health Information 26</p> <p>2.1.1 Deaths (Mortality) 26</p> <p>2.1.2 Compiling Mortality Statistics: The Example of England and Wales 28</p> <p>2.1.3 Suicide Among Men 29</p> <p>2.1.4 Suicide Among Young Women 31</p> <p>2.1.5 Variations in Deaths of Very Young Children 31</p> <p>2.2 Descriptive Epidemiology 33</p> <p>2.2.1 What is Descriptive Epidemiology? 33</p> <p>2.2.2 International Variations in Rates of Lung Cancer 33</p> <p>2.2.3 Illness (Morbidity) 34</p> <p>2.2.4 Sources of Information on Morbidity 35</p> <p>2.2.5 Notification of Infectious Disease 35</p> <p>2.2.6 Illness Seen in General Practice 38</p> <p>2.3 Information on the Environment 39</p> <p>2.3.1 Air Pollution and Health 39</p> <p>2.3.2 Routinely Available Data on Air Pollution 39</p> <p>2.4 Displaying, Describing, and Presenting Data 41</p> <p>2.4.1 Displaying the Data 41</p> <p>2.4.2 Calculating the Frequency Distribution 42</p> <p>2.4.3 Describing the Frequency Distribution 44</p> <p>2.4.4 The Relative Frequency Distribution 57</p> <p>2.4.5 Scatterplots, Linear Relationships and Correlation 60</p> <p>2.5 Routinely Available Health Data 69</p> <p>2.5.1 Introduction 69</p> <p>2.5.2 Classification of Routine Health Information Sources 69</p> <p>2.5.3 Demographic Data 71</p> <p>2.5.4 Health Event Data 73</p> <p>2.5.5 Population-Based Health Information 78</p> <p>2.5.6 Deprivation Indices 79</p> <p>2.5.7 Routine Data Sources for Countries Other Than the UK Descriptive Epidemiology in Action 80 80</p> <p>2.6.1 The London Smogs of the 1950s 80</p> <p>2.6.2 Ecological Studies 82</p> <p>2.7 Overview of Epidemiological Study Designs</p> <p>2.8 Answers to Self-Assessment Exercises 84 86</p> <p><b>3 Standardisation 101</b></p> <p>Introduction and Learning Objectives 101</p> <p>3.1 Health Inequalities in Merseyside 101</p> <p>3.1.1 Socio-Economic Conditions and Health 101</p> <p>3.1.2 Comparison of Crude Death Rates 102</p> <p>3.1.3 Usefulness of a Summary Measure 104</p> <p>3.2 Indirect Standardisation: Calculation of the Standardised Mortality Ratio (SMR) 105</p> <p>3.2.1 Mortality in Liverpool 105</p> <p>3.2.2 Interpretation of the SMR 107</p> <p>3.2.3 Dealing With Random Variation: The 95 per cent Confidence Interval 107</p> <p>3.2.4 Increasing Precision of the SMR Estimate 108</p> <p>3.2.5 Mortality in Sefton 108</p> <p>3.2.6 Comparison of SMRs 110</p> <p>3.2.7 Indirectly Standardised Mortality Rates 110</p> <p>3.3 Direct Standardisation 110</p> <p>3.3.1 Introduction 110</p> <p>3.3.2 An Example: Changes in Deaths From Stroke Over Time 111</p> <p>3.3.3 Using the European Standard Population 112</p> <p>3.3.4 Direct or Indirect: Which Method is Best? 113</p> <p>3.4 Standardisation for Factors Other Than Age</p> <p>3.5 Answers to Self-Assessment Exercises 114</p> <p><b>4 Surveys 123</b></p> <p>Introduction and Learning Objectives 123</p> <p>Resource Papers 124</p> <p>4.1 Purpose and Context 124</p> <p>4.1.1 Defining the Research Question 124</p> <p>4.1.2 Political Context of Research 126</p> <p>4.2 Sampling Methods 127</p> <p>4.2.1 Introduction 127</p> <p>4.2.2 Sampling 127</p> <p>4.2.3 Probability 129</p> <p>4.2.4 Simple Random Sampling 130</p> <p>4.2.5 Stratified Sampling 131</p> <p>4.2.6 Cluster Random Sampling 132</p> <p>4.2.7 Multistage Random Sampling 133</p> <p>4.2.8 Systematic Sampling 133</p> <p>4.2.9 Convenience Sampling 133</p> <p>4.2.10 Sampling People Who are Difficult to Contact 133</p> <p>4.2.11 Quota Sampling 134</p> <p>4.2.12 Sampling in Natsal-3 135</p> <p>4.3 The Sampling Frame 137</p> <p>4.3.1 Why Do We Need a Sampling Frame? 137</p> <p>4.3.2 Losses in Sampling 137</p> <p>4.4 Sampling Error, Confidence Intervals, and Sample Size 139</p> <p>4.4.1 Sampling Distributions and the Standard Error 139</p> <p>4.4.2 The Standard Error 140</p> <p>4.4.3 Key Properties of the Normal Distribution 145</p> <p>4.4.4 Confidence Interval (CI) for the Sample Mean 146</p> <p>4.4.5 Estimating Sample Size 149</p> <p>4.4.6 Sample Size for Estimating a Population Mean 149</p> <p>4.4.7 Standard Error and 95 per cent CI for a Population Proportion 150</p> <p>4.4.8 Sample Size to Estimate a Population Proportion 151</p> <p>4.5 Response 153</p> <p>4.5.1 Determining the Response Rate 153</p> <p>4.5.2 Assessing Whether the Sample is Representative 154</p> <p>4.5.3 Maximising the Response Rate 154</p> <p>4.6 Measurement 157</p> <p>4.6.1 Introduction: The Importance of Good Measurement 157</p> <p>4.6.2 Interview or Self-Completed Questionnaire? 157</p> <p>4.6.3 Principles of Good Questionnaire Design 158</p> <p>4.6.4 Development of a Questionnaire 161</p> <p>4.6.5 Checking How Well the Interviews and Questionnaires Have Worked 161</p> <p>4.6.6 Assessing Measurement Quality 165</p> <p>4.6.7 Overview of Sources of Error 169</p> <p>4.7 Data Types and Presentation 171</p> <p>4.7.1 Introduction 171</p> <p>4.7.2 Types of Data 172</p> <p>4.7.3 Displaying and Summarising the Data 173</p> <p>4.8 Answers to Self-Assessment Exercises 176</p> <p><b>5 Cohort Studies 185</b></p> <p>Introduction and Learning Objectives 185</p> <p>Resource Papers 186</p> <p>5.1 Why Do a Cohort Study? 186</p> <p>5.1.1 Objectives of the Study 186</p> <p>5.1.2 Study Structure 188</p> <p>5.2 Obtaining the Sample 188</p> <p>5.2.1 Introduction 188</p> <p>5.2.2 Sample Size 190</p> <p>5.3 Measurement 190</p> <p>5.3.1 Importance of Good Measurement 190</p> <p>5.3.2 Identifying and Avoiding Measurement Error 190</p> <p>5.3.3 The Measurement of Blood Pressure 191</p> <p>5.3.4 Case Definition 192</p> <p>5.4 Follow-Up 193</p> <p>5.4.1 Nature of the Task 193</p> <p>5.4.2 Deaths (Mortality) 193</p> <p>5.4.3 Non-Fatal Cases (Morbidity) 194</p> <p>5.4.4 Challenges Faced with Follow-Up of a Cohort in a Different Setting 194</p> <p>5.4.5 Assessment of Changes During Follow-Up Period 196</p> <p>5.5 Basic Presentation and Analysis of Results 198</p> <p>5.5.1 Initial Presentation of Findings 198</p> <p>5.5.2 Relative Risk 199</p> <p>5.5.3 Hypothesis Test for Categorical Data: The Chi-Squared Test 201</p> <p>5.5.4 Hypothesis Tests for Continuous Data: The z-Test and the t-Test 209</p> <p>5.6 How Large Should a Cohort Study Be? 214</p> <p>5.6.1 Perils of Inadequate Sample Size 214</p> <p>5.6.2 Sample Size for a Cohort Study 215</p> <p>5.6.3 Example of Output from Sample Size Calculation 216</p> <p>5.7 Assessing Whether an Association is Causal 218</p> <p>5.7.1 The Hill Viewpoints 218</p> <p>5.7.2 Confounding: What Is It and How Can It Be Addressed? 220</p> <p>5.7.3 Does Smoking Cause Heart Disease? 222</p> <p>5.7.4 Confounding in the Physical Activity and Cancer Study 222</p> <p>5.7.5 Methods for Dealing with Confounding 224</p> <p>5.8 Simple Linear Regression 224</p> <p>5.8.1 Approaches to Describing Associations 224</p> <p>5.8.2 Finding the Best Fit for a Straight Line 226</p> <p>5.8.3 Interpreting the Regression Line 227</p> <p>5.8.4 Using the Regression Line 228</p> <p>5.8.5 Hypothesis Test of the Association Between the Explanatory and</p> <p>Outcome Variables 228</p> <p>5.8.6 How Good is the Regression Model? 229</p> <p>5.8.7 Interpreting SPSS Output for Simple Linear Regression Analysis 231</p> <p>5.8.8 First Table: Variables Entered/Removed 232</p> <p>5.9 Introduction to Multiple Linear Regression 235</p> <p>5.9.1 Principles of Multiple Regression 235</p> <p>5.9.2 Using Multivariable Linear Regression to Study Independent Associations 235</p> <p>5.9.3 Investigation of the Effect of Work Stress on Bodyweight 235</p> <p>5.9.4 Multiple Regression in the Cancer Study 239</p> <p>5.9.5 Overview of Regression Methods for Different Types of Outcome 240</p> <p>5.10 Answers to Self-Assessment Exercises 242</p> <p><b>6 Case–Control Studies 251</b></p> <p>Introduction and Learning Objectives 251</p> <p>Resource Papers 252</p> <p>6.1 Why do a Case–Control Study? 253</p> <p>6.1.1 Study Objectives 253</p> <p>6.1.2 Study Structure 254</p> <p>6.1.3 Approach to Analysis 255</p> <p>6.1.4 Retrospective Data Collection 257</p> <p>6.1.5 Applications of the Case–Control Design 258</p> <p>6.2 Key Elements of Study Design 259</p> <p>6.2.1 Selecting the Cases 259</p> <p>6.2.2 The Controls 260</p> <p>6.2.3 Exposure Assessment 262</p> <p>6.2.4 Bias in Exposure Assessment 263</p> <p>6.3 Basic Unmatched and Matched Analysis 265</p> <p>6.3.1 The Odds Ratio (OR) 265</p> <p>6.3.2 Calculation of the OR–Simple Matched Analysis 269</p> <p>6.3.3 Hypothesis Tests for Case–Control Studies 271</p> <p>6.4 Sample Size for a Case–Control Study 273</p> <p>6.4.1 Introduction 273</p> <p>6.4.2 What Information is Required? 273</p> <p>6.4.3 An Example of Sample Size Calculation Using OpenEpi 274</p> <p>6.5 Confounding and Logistic Regression 276</p> <p>6.5.1 Introduction 276</p> <p>6.5.2 Stratification 277</p> <p>6.5.3 Logistic Regression 278</p> <p>6.5.4 Example: Multivariable Logistic Regression 281</p> <p>6.5.5 Matched Studies – Conditional Logistic Regression 287</p> <p>6.5.6 Interpretation of Adjusted Results from the New Zealand Study 287</p> <p>6.6 Answers to Self-Assessment Exercises 289</p> <p><b>7 Intervention Studies 297</b></p> <p>Introduction and Learning Objectives 297</p> <p>Typology of Intervention Study Designs Described in This Chapter 297</p> <p>Terminology 298</p> <p>Resource Papers 299</p> <p>7.1 Why Do an Intervention Study? 299</p> <p>7.1.1 Study Objectives 299</p> <p>7.1.2 Structure of a Randomised, Controlled Intervention Study 300</p> <p>7.2 Key Elements of Intervention Study Design 303</p> <p>7.2.1 Defining Who Should be Included and Excluded 303</p> <p>7.2.2 Intervention and Control 304</p> <p>7.2.3 Randomisation 306</p> <p>7.2.4 Outcome Assessment 307</p> <p>7.2.5 Blinding 308</p> <p>7.2.6 Ethical Issues for Intervention Studies 308</p> <p>7.3 The Analysis of Intervention Studies 309</p> <p>7.3.1 Review of Variables at Baseline 310</p> <p>7.3.2 Loss to Follow-Up 311</p> <p>7.3.3 Compliance with the Treatment Allocation 311</p> <p>7.3.4 Analysis by Intention-to-Treat 312</p> <p>7.3.5 Analysis per Protocol 313</p> <p>7.3.6 What is the Effect of the Intervention? 313</p> <p>7.3.7 Drawing Conclusions 315</p> <p>7.3.8 Adjustment for Variables Known to Influence the Outcome 315</p> <p>7.3.9 Paired Comparisons 315</p> <p>7.3.10 The Crossover Trial 317</p> <p>7.4 Testing More-Complex Interventions 318</p> <p>7.4.1 Introduction 318</p> <p>7.4.2 Randomised Trial of Individuals for a Complex Intervention 319</p> <p>7.4.3 Factorial Design 322</p> <p>7.4.4 Analysis and Interpretation 323</p> <p>7.4.5 Departure from the Ideal Blinded RCT Design 327</p> <p>7.4.6 The Cluster Randomised Trial 328</p> <p>7.4.7 The Community (Cluster) Randomised Trial 330</p> <p>7.4.8 Non-Randomised Intervention Designs 332</p> <p>7.4.9 The Natural Experiment 333</p> <p>7.5 Analysis of Intervention Studies Using a Cluster Design 334</p> <p>7.5.1 Why Does the Use of Clusters Make a Difference? 334</p> <p>7.5.2 Summarising Clustering Effects: The Intra-Class Correlation Coefficient 334</p> <p>7.5.3 Multi-Level Modelling 335</p> <p>7.5.4 Analysis of the Cluster RCT of Physical Activity 335</p> <p>7.6 How Big Should the Intervention Study Be? 337</p> <p>7.6.1 Introduction 337</p> <p>7.6.2 Sample Size for a Trial with Categorical Data Outcomes 337</p> <p>7.6.3 One-Sided and Two-Sided Tests 339</p> <p>7.6.4 Sample Size for a Trial with Continuous Data Outcomes 339</p> <p>7.6.5 Sample Size for an Intervention Study Using Cluster Design 340</p> <p>7.6.6 Estimation of Sample Size is not a Precise Science 341</p> <p>7.7 Intervention Study Registration, Management, and Reporting 341</p> <p>7.7.1 Introduction 341</p> <p>7.7.2 Registration 342</p> <p>7.7.3 Trial Management 342</p> <p>7.7.4 Reporting Standards (CONSORT) 343</p> <p>7.8 Answers to Self-Assessment Exercises 344</p> <p><b>8 Life Tables, Survival Analysis, and Cox Regression 355</b></p> <p>Introduction and Learning Objectives 355</p> <p>Resource Papers 356</p> <p>8.1 Survival Analysis 356</p> <p>8.1.1 Introduction 356</p> <p>8.1.2 Why Do We Need Survival Analysis? 356</p> <p>8.1.3 Censoring 357</p> <p>8.1.4 Kaplan–Meier Survival Curves 359</p> <p>8.1.5 Kaplan–Meier Survival Curves 361</p> <p>8.1.6 The Log-Rank Test 362</p> <p>8.1.7 Interpretation of the Kaplan–Meier Survival Curve 365</p> <p>8.2 Cox Regression 371</p> <p>8.2.1   Introduction 371</p> <p>8.2.2 The Hazard Function 371</p> <p>8.2.3 Assumption of Proportional Hazards 372</p> <p>8.2.4 The Cox Regression Model 372</p> <p>8.2.5 Checking the Assumption of Proportional Hazards 372</p> <p>8.2.6 Interpreting the Cox Regression Model 373</p> <p>8.2.7 Prediction 374</p> <p>8.2.8 Application of Cox Regression 375</p> <p>8.3 Current Life Tables  377</p> <p>8.3.1 Introduction 377</p> <p>8.3.2 Current Life Tables and Life Expectancy at Birth 377</p> <p>8.3.3 Life Expectancy at Other Ages 379</p> <p>8.3.4 Healthy or Disability-Free Life Expectancy 379</p> <p>8.3.5 Abridged Life Tables 380</p> <p>8.3.6 Summary 381</p> <p>8.4    Answers to Self-Assessment Exercises 381</p> <p><b>9 Systematic Reviews and Meta-Analysis 385</b></p> <p>Introduction and Learning Objectives 385</p> <p>Increasing Power by Combining Studies 386</p> <p>Resource Papers 387</p> <p>9.1 The Why and How of Systematic Reviews 387</p> <p>9.1.1 Why is it Important that Reviews be Systematic? 387</p> <p>9.1.2 Method of Systematic Review – Overview and Developing a Protocol 388</p> <p>9.1.3 Deciding on the Research Question and Objectives for the Review 389</p> <p>9.1.4 Defining Criteria for Inclusion and Exclusion of Studies 390</p> <p>9.1.5 Identifying Relevant Studies 391</p> <p>9.1.6 Assessment of Methodological Quality 396</p> <p>9.1.7 Extracting Data 399</p> <p>9.1.8 Describing the Results 399</p> <p>9.2  The Methodology of Meta-Analysis  402</p> <p>9.2.1 Method of Meta-Analysis – Overview 402</p> <p>9.2.2 Assessment of Publication Bias – the Funnel Plot 403</p> <p>9.2.3 Heterogeneity 405</p> <p>9.2.4 Calculating the Pooled Estimate 407</p> <p>9.2.5 Presentation of Results: Forest Plot 408</p> <p>9.2.6 Sensitivity Analysis 409</p> <p>9.2.7 Statistical Software for the Conduct of Meta-Analysis 410</p> <p>9.2.8 Another Example of the Value of Meta-Analysis – Identifying a Dangerous Treatment 411</p> <p>9.3 Systematic Reviews and Meta-Analyses of Observational Studies  414</p> <p>9.3.1 Introduction 414</p> <p>9.3.2 Why Conduct a Systematic Review of Observational Studies? 414</p> <p>9.3.3 Approach to Meta-Analysis of Observational Studies 415</p> <p>9.3.4 Method of Systematic Review of Observational Studies 416</p> <p>9.3.5 Method of Meta-Analysis of Observational Studies 416</p> <p>9.4 Reporting and Publishing Systematic Reviews and Meta-Analyses  418</p> <p>9.5 The Cochrane Collaboration 419</p> <p>9.5.1 Introduction 419</p> <p>9.5.2 Cochrane Collaboration Logo 422</p> <p>Collaborative Review Groups 422</p> <p>9.5.3 Cochrane Library 422</p> <p>9.6 Answers to Self-Assessment Exercises 423</p> <p><b>10 Prevention Strategies and Evaluation of Screening 429</b></p> <p>Introduction and Learning Objectives 429</p> <p>Resource Papers 430</p> <p>10.1 Concepts of Risk 430</p> <p>10.1.1 Relative and Attributable Risk 430</p> <p>10.1.2 Calculation of AR 431</p> <p>10.1.3 Attributable Fraction (AF) for a Dichotomous Exposure 432</p> <p>10.1.4 Attributable Fraction for Continuous and Multiple Category Exposures 434</p> <p>10.1.5 Years of Life Lost (YLL) and Years Lived with Disability (YLD) 434</p> <p>10.1.6 Disability-Adjusted Life Years (DALYs) 436</p> <p>10.1.7 Burden Attributable to Specific Risk Factors 438</p> <p>10.2 Strategies of Prevention 440</p> <p>10.2.1 The Distribution of Risk in Populations 440</p> <p>10.2.2 High-Risk and Population Approaches to Prevention 443</p> <p>10.2.3 Safety and the Population Strategy 446</p> <p>10.2.4 The High-Risk and Population Strategies Revisited 447</p> <p>10.2.5 Implications of Genomic Research for Disease Prevention 448</p> <p>10.3 Evaluation of Screening Programmes 450</p> <p>10.3.1 Purpose of Screening 451</p> <p>10.3.2 Criteria for Programme Evaluation 451</p> <p>10.3.3 Assessing Validity of a Screening Test 452</p> <p>10.3.4 Methodological Issues in Studies of Screening Programme Effectiveness 460</p> <p>10.3.5 Are the Wilson–Jungner Criteria Relevant Today? 461</p> <p>10.4 Cohort and Period Effects 463</p> <p>10.4.1 Analysis of Change in Risk Over Time 463</p> <p>10.4.2 Example: Suicide Trends in UK Men and Women 464</p> <p>10.5 Answers to Self-Assessment Exercises 468</p> <p><b>11 Probability Distributions, Hypothesis Testing, and Bayesian Methods 477</b></p> <p>Introduction and Learning Objectives 477</p> <p>Resource Papers 478</p> <p>11.1 Probability Distributions 478</p> <p>11.1.1 Probability – A Brief Review 478</p> <p>11.1.2 Introduction to Probability Distributions 479</p> <p>11.1.3 Types of Probability Distribution 481</p> <p>11.1.4 Probability Distributions: Implications for Statistical Methods 487</p> <p>11.2 Data That Do Not Fit a Probability Distribution 488</p> <p>11.2.1 Robustness of an Hypothesis Test 488</p> <p>11.2.2 Transforming the Data 488</p> <p>11.2.3 Principles of Non-Parametric Hypothesis Testing 492</p> <p>11.3 Hypothesis Testing: Summary of Common Parametric and Non-Parametric Methods 493</p> <p>11.3.1 Introduction 493</p> <p>11.3.2 Review of Hypothesis Tests 494</p> <p>11.3.3 Fundamentals of Hypothesis Testing 494</p> <p>11.3.4 Summary: Stages of Hypothesis Testing 495</p> <p>11.3.5 Comparing Two Independent Groups 496</p> <p>11.3.6 Comparing Two Paired (or Matched) Groups 500</p> <p>11.3.7 Testing for Association Between Two Groups 506</p> <p>11.3.8 Comparing More Than Two Groups 508</p> <p>11.3.9 Association Between Categorical Variables 513</p> <p>11.4 Choosing an Appropriate Hypothesis Test 517</p> <p>11.4.1 Introduction 517</p> <p>11.4.2 Using a Guide Table for Selecting a Hypothesis Test 517</p> <p>11.4.3 The Problem of Multiple Significance Testing 520</p> <p>11.5 Bayesian Methods 520</p> <p>11.5.1 Introduction: A Different Approach to Inference 520</p> <p>11.5.2 Bayes’ Theorem and Formula 521</p> <p>11.5.3 Application and Relevance 522</p> <p>11.6 Answers to Self-Assessment Exercises 525</p> <p>Bibliography 529</p> <p>Index 533</p>

<p> <strong>Nigel Bruce, PhD</strong> is Emeritus Professor of Public Health at the Department of Public Health and Policy, University of Liverpool, UK. <p> <strong>Daniel Pope, PhD</strong> is Senior Lecturer in Epidemiology and Public Health at the Department of Public Health and Policy, University of Liverpool, UK. <p> <strong>Debbi Stanistreet, PhD</strong> is Senior Lecturer and Faculty Director of Widening Participation at the Department of Public Health and Policy, University of Liverpool, UK.

<p><b> A practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community </b> <p> This comprehensive text, which has been extensively revised with new material and additional topics, utilizes a practical slant to introduce health professionals and students to epidemiology, biostatistics, and research methodology. It draws examples from a wide range of topics, covering all of the main contemporary health research methods, including survival analysis, Cox regression, and systematic reviews and meta-analysis—the explanation of which go beyond introductory concepts. This second edition of <em>Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics</em> also helps develop critical skills that will prepare students to move on to more advanced and specialized methods. <p> A clear distinction is made between knowledge and concepts that all students should ensure they understand, and those that can be pursued further by those who wish to do so. Self-assessment exercises throughout the text help students explore and reflect on their understanding. A program of practical exercises in SPSS (using a prepared data set) helps to consolidate the theory and develop skills and confidence in data handling, analysis, and interpretation. Highlights of the book include: <ul> <li>Combining epidemiology and bio-statistics to demonstrate the relevance and strength of statistical methods</li> <li>Emphasis on the interpretation of statistics using examples from a variety of public health and health care situations to stress relevance and application</li> <li>Use of concepts related to examples of published research to show the application of methods and balance between ideals and the realities of research in practice</li> <li>Integration of practical data analysis exercises to develop skills and confidence</li> <li>Supplementation by a student companion website which provides guidance on data handling in SPSS and study data sets as referred to in the text</li> </ul> <br> <p> <em>Quantitative Methods for Health Research, Second Edition</em> is a practical learning resource for students, practitioners and researchers in public health, health care and related disciplines, providing both a course book and a useful introductory reference.

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