Quantitative Methods for Health Research

A Practical Interactive Guide to Epidemiology and Statistics
 
 
Wiley-Blackwell (Verlag)
  • 1. Auflage
  • |
  • erschienen am 29. November 2017
  • |
  • 568 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-118-66526-8 (ISBN)
 

A practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community

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 Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics also helps develop critical skills that will prepare students to move on to more advanced and specialized methods.

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:

  • Combining epidemiology and bio-statistics to demonstrate the relevance and strength of statistical methods
  • Emphasis on the interpretation of statistics using examples from a variety of public health and health care situations to stress relevance and application
  • 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
  • Integration of practical data analysis exercises to develop skills and confidence
  • Supplementation by a student companion website which provides guidance on data handling in SPSS and study data sets as referred to in the text

Quantitative Methods for Health Research, Second Edition 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.



Nigel Bruce, PhD is Emeritus Professor of Public Health at the Department of Public Health and Policy, University of Liverpool, UK.

Daniel Pope, PhD is Senior Lecturer in Epidemiology and Public Health at the Department of Public Health and Policy, University of Liverpool, UK.

Debbi Stanistreet, PhD is Senior Lecturer and Faculty Director of Widening Participation at the Department of Public Health and Policy, University of Liverpool, UK.

  • Englisch
  • Newark
  • |
  • Großbritannien
John Wiley & Sons Inc
  • Für Beruf und Forschung
  • 11,62 MB
978-1-118-66526-8 (9781118665268)
1118665260 (1118665260)
weitere Ausgaben werden ermittelt
  • Intro
  • Quantitative Methods for Health Research
  • Contents
  • Preface
  • Introduction
  • Learning Objectives
  • Resource Papers and Information Sources
  • Key Terms
  • Sample Size Calculations
  • SPSS Dataset Used for Illustrating Examples of Statistical Analysis
  • Self-Assessment Exercises
  • Mathematical Aspects of Statistics
  • Organisation of Subject Matter by Chapter
  • Acknowledgements
  • About the Companion Website
  • 1 Philosophy of Science and Introduction to Epidemiology
  • Introduction and Learning Objectives
  • 1.1 Approaches to Scientific Research
  • 1.1.1 History and Nature of Scientific Research
  • 1.1.2 What is Epidemiology?
  • 1.1.3 What are Statistics?
  • 1.1.4 Approach to Learning
  • 1.2 Formulating a Research Question
  • 1.2.1 Importance of a Well-Defined Research Question
  • 1.2.2 Development of Research Ideas
  • 1.3 Rates: Incidence and Prevalence
  • 1.3.1 Why Do We Need Rates?
  • 1.3.2 Measures of Disease Frequency
  • 1.3.3 Prevalence Rate
  • 1.3.4 Incidence Rate
  • 1.3.5 Relationship Between Incidence, Duration, and Prevalence
  • 1.4 Concepts of Prevention
  • 1.4.1 Introduction
  • 1.4.2 Primary, Secondary, and Tertiary Prevention
  • 1.5 Answers to Self-Assessment Exercises
  • 2 Routine Data Sources and Descriptive Epidemiology
  • Introduction and Learning Objectives
  • 2.1 Routine Collection of Health Information
  • 2.1.1 Deaths (Mortality)
  • 2.1.2 Compiling Mortality Statistics: The Example of England and Wales
  • 2.1.3 Suicide Among Men
  • 2.1.4 Suicide Among Young Women
  • 2.1.5 Variations in Deaths of Very Young Children
  • 2.2 Descriptive Epidemiology
  • 2.2.1 What is Descriptive Epidemiology?
  • 2.2.2 International Variations in Rates of Lung Cancer
  • 2.2.3 Illness (Morbidity)
  • 2.2.4 Sources of Information on Morbidity
  • 2.2.5 Notification of Infectious Disease
  • 2.2.6 Illness Seen in General Practice
  • 2.3 Information on the Environment
  • 2.3.1 Air Pollution and Health
  • 2.3.2 Routinely Available Data on Air Pollution
  • 2.4 Displaying, Describing, and Presenting Data
  • 2.4.1 Displaying the Data
  • 2.4.2 Calculating the Frequency Distribution
  • 2.4.3 Describing the Frequency Distribution
  • 2.4.4 The Relative Frequency Distribution
  • 2.4.5 Scatterplots, Linear Relationships and Correlation
  • 2.5 Routinely Available Health Data
  • 2.5.1 Introduction
  • 2.5.2 Classification of Routine Health Information Sources
  • 2.5.3 Demographic Data
  • 2.5.4 Health Event Data
  • 2.5.5 Population-Based Health Information
  • 2.5.6 Deprivation Indices
  • 2.5.7 Routine Data Sources for Countries Other Than the UK
  • 2.6 Descriptive Epidemiology in Action
  • 2.6.1 The London Smogs of the 1950s
  • 2.6.2 Ecological Studies
  • 2.7 Overview of Epidemiological Study Designs
  • 2.8 Answers to Self-Assessment Exercises
  • 3 Standardisation
  • Introduction and Learning Objectives
  • 3.1 Health Inequalities in Merseyside
  • 3.1.1 Socio-Economic Conditions and Health
  • 3.1.2 Comparison of Crude Death Rates
  • 3.1.3 Usefulness of a Summary Measure
  • 3.2 Indirect Standardisation: Calculation of the Standardised Mortality Ratio (SMR)
  • 3.2.1 Mortality in Liverpool
  • 3.2.2 Interpretation of the SMR
  • 3.2.3 Dealing With Random Variation: The 95 per cent Confidence Interval
  • 3.2.4 Increasing Precision of the SMR Estimate
  • 3.2.5 Mortality in Sefton
  • 3.2.6 Comparison of SMRs
  • 3.2.7 Indirectly Standardised Mortality Rates
  • 3.3 Direct Standardisation
  • 3.3.1 Introduction
  • 3.3.2 An Example: Changes in Deaths From Stroke Over Time
  • 3.3.3 Using the European Standard Population
  • 3.3.4 Direct or Indirect: Which Method is Best?
  • 3.4 Standardisation for Factors Other Than Age
  • 3.5 Answers to Self-Assessment Exercises
  • 4 Surveys
  • Introduction and Learning Objectives
  • Resource Papers
  • 4.1 Purpose and Context
  • 4.1.1 Defining the Research Question
  • 4.1.2 Political Context of Research
  • 4.2 Sampling Methods
  • 4.2.1 Introduction
  • 4.2.2 Sampling
  • 4.2.3 Probability
  • 4.2.4 Simple Random Sampling
  • 4.2.5 Stratified Sampling
  • 4.2.6 Cluster Random Sampling
  • 4.2.7 Multistage Random Sampling
  • 4.2.8 Systematic Sampling
  • 4.2.9 Convenience Sampling
  • 4.2.10 Sampling People Who are Difficult to Contact
  • 4.2.11 Quota Sampling
  • 4.2.12 Sampling in Natsal-3
  • 4.3 The Sampling Frame
  • 4.3.1 Why Do We Need a Sampling Frame?
  • 4.3.2 Losses in Sampling
  • 4.4 Sampling Error, Confidence Intervals, and Sample Size
  • 4.4.1 Sampling Distributions and the Standard Error
  • 4.4.2 The Standard Error
  • 4.4.3 Key Properties of the Normal Distribution
  • 4.4.4 Confidence Interval (CI) for the Sample Mean
  • 4.4.5 Estimating Sample Size
  • 4.4.6 Sample Size for Estimating a Population Mean
  • 4.4.7 Standard Error and 95 per cent CI for a Population Proportion
  • 4.4.8 Sample Size to Estimate a Population Proportion
  • 4.5 Response
  • 4.5.1 Determining the Response Rate
  • 4.5.2 Assessing Whether the Sample is Representative
  • 4.5.3 Maximising the Response Rate
  • 4.6 Measurement
  • 4.6.1 Introduction: The Importance of Good Measurement
  • 4.6.2 Interview or Self-Completed Questionnaire?
  • 4.6.3 Principles of Good Questionnaire Design
  • 4.6.4 Development of a Questionnaire
  • 4.6.5 Checking How Well the Interviews and Questionnaires Have Worked
  • 4.6.6 Assessing Measurement Quality
  • 4.6.7 Overview of Sources of Error
  • 4.7 Data Types and Presentation
  • 4.7.1 Introduction
  • 4.7.2 Types of Data
  • 4.7.3 Displaying and Summarising the Data
  • 4.8 Answers to Self-Assessment Exercises
  • 5 Cohort Studies
  • Introduction and Learning Objectives
  • Resource Papers
  • 5.1 Why Do a Cohort Study?
  • 5.1.1 Objectives of the Study
  • 5.1.2 Study Structure
  • 5.2 Obtaining the Sample
  • 5.2.1 Introduction
  • 5.2.2 Sample Size
  • 5.3 Measurement
  • 5.3.1 Importance of Good Measurement
  • 5.3.2 Identifying and Avoiding Measurement Error
  • 5.3.3 The Measurement of Blood Pressure
  • 5.3.4 Case Definition
  • 5.4 Follow-Up
  • 5.4.1 Nature of the Task
  • 5.4.2 Deaths (Mortality)
  • 5.4.3 Non-Fatal Cases (Morbidity)
  • 5.4.4 Challenges Faced with Follow-Up of a Cohort in a Different Setting
  • 5.4.5 Assessment of Changes During Follow-Up Period
  • 5.5 Basic Presentation and Analysis of Results
  • 5.5.1 Initial Presentation of Findings
  • 5.5.2 Relative Risk
  • 5.5.3 Hypothesis Test for Categorical Data: The Chi-Squared Test
  • 5.5.4 Hypothesis Tests for Continuous Data: The z-Test and the t-Test
  • 5.6 How Large Should a Cohort Study Be?
  • 5.6.1 Perils of Inadequate Sample Size
  • 5.6.2 Sample Size for a Cohort Study
  • 5.6.3 Example of Output from Sample Size Calculation
  • 5.7 Assessing Whether an Association is Causal
  • 5.7.1 The Hill Viewpoints
  • 5.7.2 Confounding: What Is It and How Can It Be Addressed?
  • 5.7.3 Does Smoking Cause Heart Disease?
  • 5.7.4 Confounding in the Physical Activity and Cancer Study
  • 5.7.5 Methods for Dealing with Confounding
  • 5.8 Simple Linear Regression
  • 5.8.1 Approaches to Describing Associations
  • 5.8.2 Finding the Best Fit for a Straight Line
  • 5.8.3 Interpreting the Regression Line
  • 5.8.4 Using the Regression Line
  • 5.8.5 Hypothesis Test of the Association Between the Explanatory and Outcome Variables
  • 5.8.6 How Good is the Regression Model?
  • 5.8.7 Interpreting SPSS Output for Simple Linear Regression Analysis
  • 5.8.8 First Table: Variables Entered/Removed
  • 5.9 Introduction to Multiple Linear Regression
  • 5.9.1 Principles of Multiple Regression
  • 5.9.2 Using Multivariable Linear Regression to Study Independent Associations
  • 5.9.3 Investigation of the Effect of Work Stress on Bodyweight
  • 5.9.4 Multiple Regression in the Cancer Study
  • 5.9.5 Overview of Regression Methods for Different Types of Outcome
  • 5.10 Answers to Self-Assessment Exercises
  • 6 Case-Control Studies
  • Introduction and Learning Objectives
  • Resource Papers
  • 6.1 Why do a Case-Control Study?
  • 6.1.1 Study Objectives
  • 6.1.2 Study Structure
  • 6.1.3 Approach to Analysis
  • 6.1.4 Retrospective Data Collection
  • 6.1.5 Applications of the Case-Control Design
  • 6.2 Key Elements of Study Design
  • 6.2.1 Selecting the Cases
  • 6.2.2 The Controls
  • 6.2.3 Exposure Assessment
  • 6.2.4 Bias in Exposure Assessment
  • 6.3 Basic Unmatched and Matched Analysis
  • 6.3.1 The Odds Ratio (OR)
  • 6.3.2 Calculation of the OR-Simple Matched Analysis
  • 6.3.3 Hypothesis Tests for Case-Control Studies
  • 6.4 Sample Size for a Case-Control Study
  • 6.4.1 Introduction
  • 6.4.2 What Information is Required?
  • 6.4.3 An Example of Sample Size Calculation Using OpenEpi
  • 6.5 Confounding and Logistic Regression
  • 6.5.1 Introduction
  • 6.5.2 Stratification
  • 6.5.3 Logistic Regression
  • 6.5.4 Example: Multivariable Logistic Regression
  • 6.5.5 Matched Studies - Conditional Logistic Regression
  • 6.5.6 Interpretation of Adjusted Results from the New Zealand Study
  • 6.6 Answers to Self-Assessment Exercises
  • 7 Intervention Studies
  • Introduction and Learning Objectives
  • Typology of Intervention Study Designs Described in This Chapter
  • Terminology
  • Resource Papers
  • Principal References
  • Supplementary References
  • 7.1 Why Do an Intervention Study?
  • 7.1.1 Study Objectives
  • 7.1.2 Structure of a Randomised, Controlled Intervention Study
  • 7.2 Key Elements of Intervention Study Design
  • 7.2.1 Defining Who Should be Included and Excluded
  • 7.2.2 Intervention and Control
  • 7.2.3 Randomisation
  • 7.2.4 Outcome Assessment
  • 7.2.5 Blinding
  • 7.2.6 Ethical Issues for Intervention Studies
  • 7.3 The Analysis of Intervention Studies
  • 7.3.1 Review of Variables at Baseline
  • 7.3.2 Loss to Follow-Up
  • 7.3.3 Compliance with the Treatment Allocation
  • 7.3.4 Analysis by Intention-to-Treat
  • 7.3.5 Analysis per Protocol
  • 7.3.6 What is the Effect of the Intervention?
  • 7.3.7 Drawing Conclusions
  • 7.3.8 Adjustment for Variables Known to Influence the Outcome
  • 7.3.9 Paired Comparisons
  • 7.3.10 The Crossover Trial
  • 7.4 Testing More-Complex Interventions
  • 7.4.1 Introduction
  • 7.4.2 Randomised Trial of Individuals for a Complex Intervention
  • 7.4.3 Factorial Design
  • 7.4.4 Analysis and Interpretation
  • 7.4.5 Departure from the Ideal Blinded RCT Design
  • 7.4.6 The Cluster Randomised Trial
  • 7.4.7 The Community (Cluster) Randomised Trial
  • 7.4.8 Non-Randomised Intervention Designs
  • 7.4.9 The Natural Experiment
  • 7.5 Analysis of Intervention Studies Using a Cluster Design
  • 7.5.1 Why Does the Use of Clusters Make a Difference?
  • 7.5.2 Summarising Clustering Effects: The Intra-Class Correlation Coefficient
  • 7.5.3 Multi-Level Modelling
  • 7.5.4 Analysis of the Cluster RCT of Physical Activity
  • 7.6 How Big Should the Intervention Study Be?
  • 7.6.1 Introduction
  • 7.6.2 Sample Size for a Trial with Categorical Data Outcomes
  • 7.6.3 One-Sided and Two-Sided Tests
  • 7.6.4 Sample Size for a Trial with Continuous Data Outcomes
  • 7.6.5 Sample Size for an Intervention Study Using Cluster Design
  • 7.6.6 Estimation of Sample Size is not a Precise Science
  • 7.7 Intervention Study Registration, Management, and Reporting
  • 7.7.1 Introduction
  • 7.7.2 Registration
  • 7.7.3 Trial Management
  • 7.7.4 Reporting Standards (CONSORT)
  • 7.8 Answers to Self-Assessment Exercises
  • 8 Life Tables, Survival Analysis, and Cox Regression
  • Introduction and Learning Objectives
  • Resource Papers
  • 8.1 Survival Analysis
  • 8.1.1 Introduction
  • 8.1.2 Why Do We Need Survival Analysis?
  • 8.1.3 Censoring
  • 8.1.4 Kaplan-Meier Survival Curves
  • 8.1.5 Kaplan-Meier Survival Curves
  • 8.1.6 The Log-Rank Test
  • 8.1.7 Interpretation of the Kaplan-Meier Survival Curve
  • 8.2 Cox Regression
  • 8.2.1 Introduction
  • 8.2.2 The Hazard Function
  • 8.2.3 Assumption of Proportional Hazards
  • 8.2.4 The Cox Regression Model
  • 8.2.5 Checking the Assumption of Proportional Hazards
  • 8.2.6 Interpreting the Cox Regression Model
  • 8.2.7 Prediction
  • 8.2.8 Application of Cox Regression
  • 8.3 Current Life Tables
  • 8.3.1 Introduction
  • 8.3.2 Current Life Tables and Life Expectancy at Birth
  • 8.3.3 Life Expectancy at Other Ages
  • 8.3.4 Healthy or Disability-Free Life Expectancy
  • 8.3.5 Abridged Life Tables
  • 8.3.6 Summary
  • 8.4 Answers to Self-Assessment Exercises
  • 9 Systematic Reviews and Meta-Analysis
  • Introduction and Learning Objectives
  • Increasing Power by Combining Studies
  • Resource Papers
  • 9.1 The Why and How of Systematic Reviews
  • 9.1.1 Why is it Important that Reviews be Systematic?
  • 9.1.2 Method of Systematic Review - Overview and Developing a Protocol
  • 9.1.3 Deciding on the Research Question and Objectives for the Review
  • 9.1.4 Defining Criteria for Inclusion and Exclusion of Studies
  • 9.1.5 Identifying Relevant Studies
  • 9.1.6 Assessment of Methodological Quality
  • 9.1.7 Extracting Data
  • 9.1.8 Describing the Results
  • 9.2 The Methodology of Meta-Analysis
  • 9.2.1 Method of Meta-Analysis - Overview
  • 9.2.2 Assessment of Publication Bias - the Funnel Plot
  • 9.2.3 Heterogeneity
  • 9.2.4 Calculating the Pooled Estimate
  • 9.2.5 Presentation of Results: Forest Plot
  • 9.2.6 Sensitivity Analysis
  • 9.2.7 Statistical Software for the Conduct of Meta-Analysis
  • 9.2.8 Another Example of the Value of Meta-Analysis - Identifying a Dangerous Treatment
  • 9.3 Systematic Reviews and Meta-Analyses of Observational Studies
  • 9.3.1 Introduction
  • 9.3.2 Why Conduct a Systematic Review of Observational Studies?
  • 9.3.3 Approach to Meta-Analysis of Observational Studies
  • 9.3.4 Method of Systematic Review of Observational Studies
  • 9.3.5 Method of Meta-Analysis of Observational Studies
  • 9.4 Reporting and Publishing Systematic Reviews and Meta-Analyses
  • 9.5 The Cochrane Collaboration
  • 9.5.1 Introduction
  • 9.5.2 Cochrane Collaboration Logo
  • 9.5.3 Collaborative Review Groups
  • 9.5.4 Cochrane Library
  • 9.6 Answers to Self-Assessment Exercises
  • 10 Prevention Strategies and Evaluation of Screening
  • Introduction and Learning Objectives
  • Resource Papers
  • 10.1 Concepts of Risk
  • 10.1.1 Relative and Attributable Risk
  • 10.1.2 Calculation of AR
  • 10.1.3 Attributable Fraction (AF) for a Dichotomous Exposure
  • 10.1.4 Attributable Fraction for Continuous and Multiple Category Exposures
  • 10.1.5 Years of Life Lost (YLL) and Years Lived with Disability (YLD)
  • 10.1.6 Disability-Adjusted Life Years (DALYs)
  • 10.1.7 Burden Attributable to Specific Risk Factors
  • 10.2 Strategies of Prevention
  • 10.2.1 The Distribution of Risk in Populations
  • 10.2.2 High-Risk and Population Approaches to Prevention
  • 10.2.3 Safety and the Population Strategy
  • 10.2.4 The High-Risk and Population Strategies Revisited
  • 10.2.5 Implications of Genomic Research for Disease Prevention
  • 10.3 Evaluation of Screening Programmes
  • 10.3.1 Purpose of Screening
  • 10.3.2 Criteria for Programme Evaluation
  • 10.3.3 Assessing Validity of a Screening Test
  • 10.3.4 Methodological Issues in Studies of Screening Programme Effectiveness
  • 10.3.5 Are the Wilson-Jungner Criteria Relevant Today?
  • 10.4 Cohort and Period Effects
  • 10.4.1 Analysis of Change in Risk Over Time
  • 10.4.2 Example: Suicide Trends in UK Men and Women
  • 10.5 Answers to Self-Assessment Exercises
  • 11 Probability Distributions, Hypothesis Testing, and Bayesian Methods
  • Introduction and Learning Objectives
  • Resource Papers
  • 11.1 Probability Distributions
  • 11.1.1 Probability - A Brief Review
  • 11.1.2 Introduction to Probability Distributions
  • 11.1.3 Types of Probability Distribution
  • 11.1.4 Probability Distributions: Implications for Statistical Methods
  • 11.2 Data That Do Not Fit a Probability Distribution
  • 11.2.1 Robustness of an Hypothesis Test
  • 11.2.2 Transforming the Data
  • 11.2.3 Principles of Non-Parametric Hypothesis Testing
  • 11.3 Hypothesis Testing: Summary of Common Parametric and Non-Parametric Methods
  • 11.3.1 Introduction
  • 11.3.2 Review of Hypothesis Tests
  • 11.3.3 Fundamentals of Hypothesis Testing
  • 11.3.4 Summary: Stages of Hypothesis Testing
  • 11.3.5 Comparing Two Independent Groups
  • 11.3.6 Comparing Two Paired (or Matched) Groups
  • 11.3.7 Testing for Association Between Two Groups
  • 11.3.8 Comparing More Than Two Groups
  • 11.3.9 Association Between Categorical Variables
  • 11.4 Choosing an Appropriate Hypothesis Test
  • 11.4.1 Introduction
  • 11.4.2 Using a Guide Table for Selecting a Hypothesis Test
  • 11.4.3 The Problem of Multiple Significance Testing
  • 11.5 Bayesian Methods
  • 11.5.1 Introduction: A Different Approach to Inference
  • 11.5.2 Bayes' Theorem and Formula
  • 11.5.3 Application and Relevance
  • 11.6 Answers to Self-Assessment Exercises
  • Bibliography
  • Index
  • EULA

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