Quantitative methods for health research a practical interactive guide to epidemiology and statistics
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 epid...
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| Médium: | E-kniha |
| Jazyk: | angličtina |
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Newark
Wiley
01.01.2018
John Wiley & Sons, Incorporated Wiley-Blackwell |
| Vydání: | 2nd ed |
| Témata: | |
| ISBN: | 9781118665411, 1118665414, 9781118665404, 1118665406 |
| On-line přístup: | Získat plný text |
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- 7.2.1 Defining Who Should be Included and Excluded
- 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
- 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
- 5.9.2 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
- 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